Service Composition and Optimal Selection of Low-Carbon Cloud Manufacturing Based on NSGA-II-SA Algorithm

: As a new model of networked manufacturing services, cloud manufacturing (CMfg) aims to allocate enterprise manufacturing resources, realize rational utilization of manufacturing resources, and adapt to increasingly complex user needs. However, previous studies on service composition and optimal selection (SCOS) in CMfg environments do not incorporate carbon emissions into the quality of service (QoS) evaluation indicators. Therefore, a SCOS model for CMfg under a low-carbon environment is ﬁrstly proposed in this paper. Secondly, based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) algorithm, a hybrid multi-objective evolutionary algorithm, named the NSGA-II-SA algorithm, is proposed to solve the model and obtain the Pareto optimal solution set. Then, an algorithm result optimization strategy combining subjective and objective is proposed to ﬁlter the Pareto optimal solution set, so as to make the ﬁnal decision. Finally, taking natural gas cylinder head production as an example, the proposed algorithm is compared with other algorithms, and the results show that the proposed algorithm can obtain more non-dominated solutions, and the quality of the solutions in the four dimensions is better than the other. Therefore, it is proved that the proposed algorithm has better comprehensive performance in SCOS under a low-carbon environment.


Introduction
Since the 21st century, a variety of advanced manufacturing models have emerged, such as agile manufacturing [1], service-oriented manufacturing [2], concurrent engineering [3], networked manufacturing [4], and cloud manufacturing (CMfg) [5].CMfg as a new networked manufacturing mode based on service-oriented thinking and integrates existing networked manufacturing and service technologies with emerging technologies such as cloud computing [6], big data [7], and the Internet of Things [8], to provide users with various high-quality and fast on-demand manufacturing services with the goal of agility and service-orientation.This new manufacturing mode can help enterprises reduce production costs, shorten the batch production cycles of new products, and make more rational and efficient use of social manufacturing resources at the macro level.However, it is very important to scientifically and effectively select the service composition with the best quality of service (QoS) and meet the needs of users in the massive CMfg service composition.
At the same time, with the development of the economy and technology, greenhouse gas has seriously exceeded the standard, resulting in the continuous increase in the greenhouse effect, which has a negative impact on the global climate.As the most important part of greenhouse gas, reducing carbon dioxide emissions is regarded as the most essential way to solve the climate problem [9].As the second largest economy in the world, China has actively undertaken the responsibility of a significant country in addressing the climate change problem and promoted the construction of my country's ecological civilization and high-quality development.At present, it has entered a critical period and a window period.China aims to have CO 2 emissions peak before 2030 and achieve carbon neutrality before 2060 [10].According to the data, the manufacturing industry has the largest carbon emissions, which accounts for 50% of the total global carbon emissions.Controlling the carbon emissions generated in the production process has become an important method for manufacturing enterprises to respond to low-carbon policies and improve their own economic benefits, as well as social and environmental benefits, in recent years [11].
SCOS is the core function of the CMfg platform.It meets the user's complex manufacturing task requirements through service composition, and adopts a scientific optimization selection method to select high-quality cloud services from the candidate service set.The topic of SCOS has been extensively studied over the past decade.Bouzary et al. [12] have successfully retrieved service candidate sets using three classification algorithms and through extracting TF-IDF vectors from the manufacturing capability data.Then, the optimal service composition problem was solved for each scenario, using two well-known metaheuristic algorithms.Jiang et al. [13] built a cloud-based bi-objective disassembly scheduling model to provide customers with multiple solutions to choose from the Pareto optimal solutions that discuss the two objectives of minimizing the makespan and the cost of disassembly services.Seghir et al. [14] addressed the QoS-aware cloud service composition problem, and proposed a Hybrid Genetic Algorithm (HGA) to solve it.The parameter settings of HGA were tuned and calibrated using the taguchi method of design of experiment, and he suggested the optimal values of these parameters.Jatoth et al. [15] presented an Optimal Fitness Aware Cloud Service Composition (OFASC) using an Adaptive Genotype Evolution based Genetic Algorithm (AGEGA) dealing with multiple QoS parameters and providing the solutions that satisfy the balancing of QoS parameters and the connectivity constraints of service composition.Que et al. [16] proposed a new CMfg service provider-to-user (M2U) model, constructed a comprehensive mathematical evaluation model with key service quality perception indicators (time, reliability, cost, and capability), and solved the SCOS problem of manufacturing services based on an information entropy immune Genetic Algorithm.Chen Youling et al. [17] proposed an evaluation index system based on six indicators, namely time, cost, quality, quality similarity, composability, and cooperation and communication, to solve the problem of unreasonable task allocation caused by the production capacity constraints of manufacturing resources in the CMfg environment.After analyzing the characteristics of the model, an improved multi-objective evolutionary algorithm based on the decomposition-Particle Swarm Optimization Algorithm was proposed to obtain the optimal composition.Guérout et al. [18] proposed a heuristic decomposition method to optimize intermediate objectives using Mixed Integer Linear Programming (MILP), which provided a solution to the multi-objective cloud virtual machine allocation problem.Zhang et al. [19] established a mathematical model considering the four service attributes of cost, time, availability, and reliability by introducing an elite substitution strategy, and extended the Flower Pollination Algorithm with adaptive switching probability, effectively solving the problem of manufacturing service composition.Yang et al. [20] introduced the energy consumption of the manufacturing process and the energy consumption of the logistics process into the CMfg service model, proposed an energy-aware service composition optimization selection (EA-SCOS) method, and used the improved Gray Wolf Optimization Algorithm to solve the problem model.Liu et al. [21] proposed a Cluster-based Collaborative Filtering (CCF) algorithm to quantify customer preference attributes and improved the Personalization-oriented third generation Non-dominated Sorting Genetic Algorithm (PoNSGA-III).

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Hybrid Gene Algorithm
Jatoth, et al.

Adaptive Ge type Evoluti based Gene Algorithm
Que, et al.

Genetic Alg rithm
Chen, et al.

Mixed Integ Linear Progra ming
Zhang, et al.

Flower Polli tion Algorith
Yang, et al.

Grey Wolf O mizer Algorit
Liu, et al.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta In addition, an algorithm result optimization strategy combining subjective and ob tive is proposed to filter the Pareto optimal solution set from multiple perspective obtain the relative optimal solution.The contributions of this paper are summarized Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summ lated literature of the SCOS problem.

G
The current study Hy However, there are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su jective single factor when filtering the Pareto optimal solution set, and f hensively consider the service composition scheme from multiple persp filtering results are biased.Therefore, this paper proposes a novel compo zation model that takes into account the effects of carbon emissions and t In addition, an algorithm result optimization strategy combining subject tive is proposed to filter the Pareto optimal solution set from multiple p obtain the relative optimal solution.The contributions of this paper are sum Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summa lated literature of the SCOS problem.The current study Hyb A However, there are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai hensively consider the service composition scheme from multiple perspec filtering results are biased.Therefore, this paper proposes a novel compos zation model that takes into account the effects of carbon emissions and tra In addition, an algorithm result optimization strategy combining subjectiv tive is proposed to filter the Pareto optimal solution set from multiple pe obtain the relative optimal solution.The contributions of this paper are sum

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Hybrid Gene Algorithm
Jatoth, et al.

Adaptive Ge type Evoluti based Gene Algorithm
Que, et al.

Genetic Alg rithm
Chen, et al.

Mixed Integ Linear Progra ming
Zhang, et al.

Flower Polli tion Algorith
Yang, et al.

Grey Wolf O mizer Algorit
Liu, et al.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta In addition, an algorithm result optimization strategy combining subjective and ob tive is proposed to filter the Pareto optimal solution set from multiple perspective obtain the relative optimal solution.The contributions of this paper are summarized

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as:

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as:

Ge
The current study Hyb A However, there are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai hensively consider the service composition scheme from multiple perspec filtering results are biased.Therefore, this paper proposes a novel compos zation model that takes into account the effects of carbon emissions and tra In addition, an algorithm result optimization strategy combining subjectiv tive is proposed to filter the Pareto optimal solution set from multiple pe obtain the relative optimal solution.The contributions of this paper are sum

The current study
However, there are two major gaps in curren studies on the service composition and optimal sel account for carbon emissions and transportation, th on QoS evaluation indicators.Secondly, most studi jective single factor when filtering the Pareto opti hensively consider the service composition schem filtering results are biased.Therefore, this paper p zation model that takes into account the effects of In addition, an algorithm result optimization strat tive is proposed to filter the Pareto optimal soluti obtain the relative optimal solution.The contributio Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta In addition, an algorithm result optimization strategy combining subjective and ob tive is proposed to filter the Pareto optimal solution set from multiple perspective obtain the relative optimal solution.The contributions of this paper are summarized

G
The current study Hy However, there are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su jective single factor when filtering the Pareto optimal solution set, and f hensively consider the service composition scheme from multiple persp filtering results are biased.Therefore, this paper proposes a novel compo zation model that takes into account the effects of carbon emissions and t In addition, an algorithm result optimization strategy combining subject tive is proposed to filter the Pareto optimal solution set from multiple p obtain the relative optimal solution.The contributions of this paper are sum Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as:

Ge
The current study Hyb A However, there are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai hensively consider the service composition scheme from multiple perspec filtering results are biased.Therefore, this paper proposes a novel compos zation model that takes into account the effects of carbon emissions and tra In addition, an algorithm result optimization strategy combining subjectiv tive is proposed to filter the Pareto optimal solution set from multiple pe obtain the relative optimal solution.The contributions of this paper are sum

The current study
However, there are two major gaps in curren studies on the service composition and optimal sel account for carbon emissions and transportation, th on QoS evaluation indicators.Secondly, most studi jective single factor when filtering the Pareto opti hensively consider the service composition schem filtering results are biased.Therefore, this paper p zation model that takes into account the effects of In addition, an algorithm result optimization strat tive is proposed to filter the Pareto optimal soluti obtain the relative optimal solution.The contributio Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Adaptive Genotype Evolution based Genetic Algorithm
Que, et al.

Genetic Algorithm
Chen, et al.

Mixed Integer Linear Programming
Zhang, et al.

Flower Pollination Algorithm
Yang, et al.

Grey Wolf Optimizer Algorithm
Liu, et al.

Genetic Algorithm The current study
Hybrid Genetic Algorithm However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Hybrid Gene Algorithm
Jatoth, et al.

Adaptive Ge type Evoluti based Gene Algorithm
Que, et al.

Genetic Alg rithm
Chen, et al.

Mixed Integ Linear Progra ming
Zhang, et al.

Flower Polli tion Algorith
Yang, et al.

Grey Wolf O mizer Algorit
Liu, et al.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta In addition, an algorithm result optimization strategy combining subjective and ob tive is proposed to filter the Pareto optimal solution set from multiple perspective obtain the relative optimal solution.The contributions of this paper are summarized Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summ lated literature of the SCOS problem.
Table 1.A chronological literature review on the SCOS problem at a glance.Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Adaptive Genotype Evolution based Genetic Algorithm
Que, et al.

Genetic Algorithm
Chen, et al.

Mixed Integer Linear Programming
Zhang, et al.

Flower Pollination Algorithm
Yang, et al.

Grey Wolf Optimizer Algorithm
Liu, et al.

Genetic Algorithm The current study
Hybrid Genetic Algorithm However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summa lated literature of the SCOS problem.
Table 1.A chronological literature review on the SCOS problem at a glance.Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Genetic Algorithm
Jiang, et al.

Adaptive Genotype Evolution based Genetic Algorithm
Que, et al.

Genetic Algorithm
Chen, et al.

Mixed Integer Linear Programming
Zhang, et al.

Flower Pollination Algorithm
Yang, et al.

Grey Wolf Optimizer Algorithm
Liu, et al.

Genetic Algorithm The current study
Hybrid Genetic Algorithm However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta In addition, an algorithm result optimization strategy combining subjective and ob tive is proposed to filter the Pareto optimal solution set from multiple perspective obtain the relative optimal solution.The contributions of this paper are summarized   Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as:      Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta In addition, an algorithm result optimization strategy combining subjective and ob tive is proposed to filter the Pareto optimal solution set from multiple perspective obtain the relative optimal solution.The contributions of this paper are summarized   Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as:      Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Hybrid Gene Algorithm
Jatoth, et al.

Adaptive Ge type Evoluti based Gene Algorithm
Que, et al.

Genetic Alg rithm
Chen, et al.

Mixed Integ Linear Progra ming
Zhang, et al.Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: 1. Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition

The current study
However, there are two major gaps in current research.Fi studies on the service composition and optimal selection (SCOS account for carbon emissions and transportation, the related con on QoS evaluation indicators.Secondly, most studies only consi jective single factor when filtering the Pareto optimal solution hensively consider the service composition scheme from multi filtering results are biased.Therefore, this paper proposes a no zation model that takes into account the effects of carbon emiss In addition, an algorithm result optimization strategy combini tive is proposed to filter the Pareto optimal solution set from obtain the relative optimal solution.The contributions of this pap

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to Processes 2023, 11, x FOR PEER REVIEW 3 Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Hybrid Gene Algorithm
Jatoth, et al.

Adaptive Ge type Evoluti based Gene Algorithm
Que, et al.

Genetic Alg rithm
Chen, et al.

Mixed Integ Linear Progra ming
Zhang, et al.

Flower Polli tion Algorith
Yang, et al.

Grey Wolf O mizer Algorit
Liu, et al.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta In addition, an algorithm result optimization strategy combining subjective and ob tive is proposed to filter the Pareto optimal solution set from multiple perspective Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summ lated literature of the SCOS problem.Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summa lated literature of the SCOS problem.

The current study
However, there are two major gaps in current research.Fi studies on the service composition and optimal selection (SCOS account for carbon emissions and transportation, the related con on QoS evaluation indicators.Secondly, most studies only consi jective single factor when filtering the Pareto optimal solution hensively consider the service composition scheme from multi filtering results are biased.Therefore, this paper proposes a no zation model that takes into account the effects of carbon emiss In addition, an algorithm result optimization strategy combini tive is proposed to filter the Pareto optimal solution set from   Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

Hybrid Genetic Algorithm
However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.
Processes 2023, 11, x FOR PEER REVIEW 3 Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Hybrid Gene Algorithm
Jatoth, et al.

Adaptive Ge type Evoluti based Gene Algorithm
Que, et al.

Genetic Alg rithm
Chen, et al.

Mixed Integ Linear Progra ming
Zhang, et al.

Flower Polli tion Algorith
Yang, et al.

Grey Wolf O mizer Algorit
Liu, et al.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summ lated literature of the SCOS problem.Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

The current study
However, there are two major gaps in current research.Fi studies on the service composition and optimal selection (SCOS account for carbon emissions and transportation, the related con on QoS evaluation indicators.Secondly, most studies only consi jective single factor when filtering the Pareto optimal solution hensively consider the service composition scheme from multi filtering results are biased.Therefore, this paper proposes a no zation model that takes into account the effects of carbon emiss  Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the lated literature of the SCOS problem.

Hybrid Gene Algorithm
Jatoth, et al.

Adaptive Ge type Evoluti based Gene Algorithm
Que, et al.

Genetic Alg rithm
Chen, et al.

Mixed Integ Linear Progra ming
Zhang, et al.Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summarizes the related literature of the SCOS problem.

QoS Indicators Low-Carbon Consideration
Table 1.A chronological literature review on the SCOS problem at a glance.

Hybrid Gene Algorithm
Jatoth, et al.

Adaptive Ge type Evoluti based Gene Algorithm
Que, et al.

Genetic Alg rithm
Chen, et al.

Mixed Integ Linear Progra ming
Zhang, et al.

Flower Polli tion Algorith
Yang, et al.

Grey Wolf O mizer Algorit
Liu, et al.

Genetic Alg rithm The current study
Hybrid Gene Algorithm However, there are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table 1 summ lated literature of the SCOS problem.

The current study
However, there a studies on the service account for carbon em on QoS evaluation ind Hybrid Genetic Algorithm However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.In addition, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set from multiple perspectives to obtain the relative optimal solution.The contributions of this paper are summarized as: 1.
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.

2.
Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named NSGA-II-SA, is proposed to solve the model and obtain the Pareto optimal solution set.

3.
In order to meet the subjective and objective requirements of the service demander at the same time, an algorithm result optimization strategy based on the combination of triangular fuzzy number analytic hierarchy process (TFNAHP) and entropy weight method (EWM) is proposed to filter the Pareto optimal solution set, so as to make the final decisions to better meet individual needs.
The rest of this paper is organized as follows.In the next section, an overview of the SCOS problem is presented.The novel composition optimization model is provided in Section 3, and Section 4 introduces the designed solution algorithm and its main steps.In Section 5, an algorithm result optimization strategy is proposed.Section 6 compares the performance of the proposed algorithm with that of other algorithms through an example, and makes the final decision using the above optimization strategy.Finally, Section 7 concludes this study and suggests directions for future researches.

Problem Description
Different from traditional networked manufacturing, in the CMfg environment, there are three types of users in the outsourcing cloud service platform, including service demanders, service providers, and cloud platform operators [22].Service providers publish their idle resources on the cloud platform, which encapsulates them in the cloud service resource library and delivers them to the cloud operator for management [23].When the outsourcing service demander submits the outsourcing processing requirements, the CMfg service platform decomposes the tasks according to the complexity of the outsourcing manufacturing tasks, and then searches and matches the services in the cloud resource library according to the requirements of a single sub-task to obtain the candidate outsourcing service resource set.It then selects the corresponding outsourcing cloud service from each candidate outsourcing service set, according to a certain logical order, to form an outsourcing service composition sequence (OSCS), so as to generate the candidate outsourcing service composition set corresponding to the entire complex manufacturing task.Finally, the best group of outsourcing service composition is selected from the candidate outsourcing service composition set to perform the complex manufacturing tasks collaboratively.
Next, take the CMfg composition optimization mode of a certain type of natural gas cylinder head as an example to explain.The structure of the natural gas cylinder head is complex, and the processing contents can be divided into the roughing process, semi-finishing process, finishing process, combined processing process, and auxiliary process.The processing equipment includes various precision machining centers, special machines for cam hole seat ring conduits, and various auxiliary machines for cleaning, press fitting, and leak testing.According to the process sequence and equipment type, the natural gas cylinder head processing process can be decomposed into multiple independent manufacturing sub-tasks, and the SCOS can be carried out, and finally provide high-quality manufacturing services to the demander in the form of cloud services.In this paper, the above process is divided into three layers (Figure 1), and the whole service composition process is described in mathematical language.

• Top layer
The top layer is used to decompose tasks and store sub-task sets.The overall outsourcing manufacturing Task is input to the top layer as the input terminal, and it is divided into N sub tasks of outsourcing manufacturing according to its complexity, that is in which Tj represents the jth subtask of the Task, j = 1, 2, . . ., N.

Intermediate layer
The middle layer is the CMfg outsourcing service resource library, which is used to store massive outsourcing manufacturing service resources.Search and match the subtask sets T 1 , T 2 , • • • , T j , • • • , T N transmitted from the top layer to obtain each candidate outsourcing service set, and input it into the bottom layer.Each candidate outsourcing service set is expressed as: in which CSS j represents the candidate outsourcing services set corresponding to the jth subtask Tj.

• Top layer
The top layer is used to decompose tasks and store sub-task sets.The overall outsourcing manufacturing Task is input to the top layer as the input terminal, and it is divided into N sub tasks of outsourcing manufacturing according to its complexity, that is , , , , , in which Tj represents the jth subtask of the Task, j = 1, 2,…,N.

Intermediate layer
The middle layer is the CMfg outsourcing service resource library, which is used to store massive outsourcing manufacturing service resources.Search and match the sub- , , , , transmitted from the top layer to obtain each candidate outsourcing service set, and input it into the bottom layer.Each candidate outsourcing service set is expressed as:{ } CSS CSS CSS CSS   , in which j CSS represents the candidate outsourcing services set corresponding to the jth subtask Tj.

Bottom layer
The bottom layer is the optimization process of outsourcing cloud service composition (OCSC).A candidate outsourcing service is selected from each candidate outsourcing service set to form an OSCS to execute the above-mentioned overall outsourcing manufacturing Task.Therefore, the OSCS can be expressed as: where j i CS is the ith candidate outsourcing service in the jth candidate outsourcing service set, and i = 1, 2,…, M, M is the number of candidate outsourcing services in the candidate outsourcing service set j CSS .

• Bottom layer
The bottom layer is the optimization process of outsourcing cloud service composition (OCSC).A candidate outsourcing service is selected from each candidate outsourcing service set to form an OSCS to execute the above-mentioned overall outsourcing manufacturing Task.Therefore, the OSCS can be expressed as: where CS j i is the ith candidate outsourcing service in the jth candidate outsourcing service set, and i = 1, 2, . . ., M, M is the number of candidate outsourcing services in the candidate outsourcing service set CSS j .
It can be seen from the above three-layer structure that the overall outsourcing manufacturing Task has ∏ N j=1 M j possible execution paths of the OCSC, in which M j is the number of candidate outsourcing services in the jth candidate outsourcing service set CSS j , and the optimization process of the OCSC is to select the best one from the above execution paths to execute the overall Task of outsourcing manufacturing.

The Mathematical Model
In response to the strategic goal of energy conservation and emission reduction [24], this research incorporates carbon emissions into the QoS evaluation indicators for the first time, and takes into account the geographical distribution of outsourcing services.It adopts four indicators, namely service time, service cost, service carbon emissions, and service reliability, as the quality evaluation criteria for CMfg outsourcing service optimization.
For the convenience of discussion, the symbol meanings required in the process of describing the problem and establishing the model are shown in Table 2.  Service time refers to the time elapsed from the time when the service demander submits the outsourcing cloud service requirement on the outsourcing cloud platform to the completion of the execution of the outsourcing cloud service task.As outsourcing cloud services need to consider both online and offline, the service time is mainly composed of three aspects: service request response time, outsourcing processing time, and logistics transportation time, which is: where T o is the service request response time; T m is the outsourcing processing time; T t is the logistics transportation time, including the logistics transportation time T d from the service demander to the outsourcing service provider, and the logistics transportation time T p between the outsourcing service providers.
(2) Service cost SC Service cost refers to the cost paid by the service demander after obtaining the required outsourcing cloud service on the outsourcing cloud platform.The service cost is mainly composed of the online cost of the cloud platform (such as the cost of online quotation and inquiry, the service fee charged by the cloud platform, etc.), the cost of outsourcing processing, and the cost of logistics and transportation, namely: where C o is the online cost of the cloud platform; C m is the outsourcing processing cost; C t is the logistics transportation cost, including the logistics transportation cost C d from the service demander to the outsourcing service provider, and the logistics transportation cost C p between the outsourcing service providers.
(3) Service Carbon Emissions SCE CMfg service providers usually provide an automatic processing line or a part of the whole line, including multiple machining centers and auxiliary equipment.As shown in Figure 2, the carbon emissions in the production process include raw material input, removal of metal allowances, and energy consumption during processing, as well as the consumption of cutting tools, cutting fluids, and lubricating oil [25].However, in the case of SCOS, the two factors of raw material input and metal material removal are the same for each manufacturer, which can be ignored.This helps to simplify the service carbon emissions model to improve the efficiency of composition optimization.

• Energy consumption carbon emission CEE
The carbon emissions generated by energy consumption mainly come from the electrical energy consumed during processing, which consists of equipment energy consumption and other auxiliary energy consumption.The equipment will rarely be stopped in the actual production process, so this paper only considers the carbon emissions generated in the two states of equipment processing and standby.Auxiliary energy

• Energy consumption carbon emission CE E
The carbon emissions generated by energy consumption mainly come from the electrical energy consumed during processing, which consists of equipment energy consumption and other auxiliary energy consumption.The equipment will rarely be stopped in the actual production process, so this paper only considers the carbon emissions generated in the two states of equipment processing and standby.Auxiliary energy consumption includes fixed energy consumption of the workshop such as raceway motors, cutting fluid centralized treatment systems, and constant temperature and humidity systems.
• Tool wear carbon emissions CE T In general, the carbon emissions generated by tool wear are mainly calculated by apportioning the carbon emissions generated in the preparation process to each process.Since more than one tool may be used in each process, the average tool wear w k ij of that process needs to be calculated based on that process's process.Therefore, the carbon emission of tool wear can be expressed as:

•
Cutting fluid consumption carbon emission CE C The carbon emission caused by cutting fluid consumption is the apportionment of carbon emission generated during the preparation process in the processing stage.Due to the long replacement cycle of cutting fluid, it is often calculated by converting the processing time within the replacement cycle.The cutting fluid consumed by equipment k per unit time can be expressed as: Carbon emissions resulting from cutting fluid consumption can be expressed as follows: •

Machine tool lubricating oil consumption carbon emission CE O
The calculation method of carbon emission caused by lubricating oil consumption is consistent with that caused by cutting fluid consumption, which is calculated by converting the processing time within the replacement cycle.The lubricating oil consumed by equipment k per unit time can be expressed as follows: Carbon emissions generated by lubricating oil consumption can be expressed as follows: (4) Service reliability SR Service reliability refers to the reliability degree of outsourced cloud services provided by outsourcing service providers, which is specifically manifested as production capacity and product quality.Generally speaking, the quality of the product can be expressed by the quality inspection qualification rate Q r , which is defined as the ratio of the qualified quantity N q obtained by the service demander from the quality inspection of the received workpieces to the total quantity N s received by the service demander after the outsourced cloud service is completed, which is denoted as Q r = N q /N s .On the other hand, the on-schedule delivery rate D r can measure the production capacity of an enterprise and its ability to accept orders from customers.It is the ratio of the number of times S f that an outsourcing service provider delivers workpieces on the delivery date to the total number of times S that outsourcing service providers undertake services on the cloud platform, which is denoted as D r = S f /S.In summary, service reliability SR can be denoted as: where α 1 and α 2 are the weight coefficients of the influencing factors Q r and D r .Since different service demanders focus on different indicators, the weight coefficients can be adjusted according to user preferences, and α 1 + α 2 = 1.According to the above section, for the outsourced cloud service composition process, the service execution paths of each OCSC generated by the outsourcing manufacturing subtasks obtained by task decomposition are complex during the process of service composition again, which can be roughly divided into four basic composite structures: sequence, parallel, selective, and circular [26].Besides the series structure, the other three structures can be transformed into series structure.Let the simplified outsourced cloud service composition consist of N outsourced cloud services in series, and the outsourced manufacturing total Task has ∏ N j=1 M j possible outsourced cloud service composition execution paths.Then, the QoS calculation model of OSCS formed by a complete execution path of outsourced cloud service composition is shown as follows: where ST(CS j i ), SC(CS j i ), SCE(CS j i ) and SR(CS j i ) respectively represent the service time, service cost, service carbon emission and service reliability of the ith candidate outsourced service in the jth candidate outsourced service set; T o (i j ), T m (i j ), and T t (i j ), respectively, represent the service request response time, outsourced processing time, and logistics transportation time of the ith candidate outsourcing service in the jth candidate outsourcing service set.C o (i j ), C m (i j ), and C t (i j ), respectively, represent the cloud platform online cost, outsourced processing cost, and logistics transportation cost of the ith candidate outsourced service in the jth candidate outsourcing service set.CE E (i j ), CE T (i j ), CE C (i j ), and CE O (i j ), respectively, represent the energy consumption carbon emissions, tool consumption carbon emissions, cutting fluid consumption carbon emissions, and lubricating oil consumption carbon emissions of the ith candidate outsourcing service in the jth candidate outsourcing service set.Q r (i j ) and D r (i j ) represent the quality inspection qualification rate and on-schedule delivery rate of the ith candidate outsourced service in the jth candidate outsourcing service set, respectively.α 1 and α 2 are the weight coefficients of Q r (i j ) and D r (i j ), and α 1 + α 2 = 1.
The ultimate purpose of outsourced service composition optimization for CMfg is to select the most appropriate outsourcing cloud service, so as to determine the outsourcing service provider to complete the outsourced task requirements proposed by the service demander.Therefore, the optimal outsourced cloud service composition should aim at the shortest service time, the lowest service cost, the least service carbon emission, and the highest service reliability, combined with the QoS calculation method of OSCS, and meet certain constraints, and the mathematical model F is constructed as follows: where f 1 is the total service time of outsourced cloud service composition; f 2 is the total service cost of outsourced cloud service composition; f 3 is the total service carbon emission of outsourced cloud service composition; f 4 is the total service reliability of outsourced cloud service composition; i j represents the ith candidate outsourcing service in the jth candidate outsourcing service set.T max is the longest delivery time required by the outsourced demander; C max is the highest cost accepted by the outsourced demander; CE max is the highest carbon emission accepted by the outsourced demander; R min is the minimum reliability required by the outsourced demander.

The Proposed Algorithm
The traditional NSGA-II is one of the best multi-objective genetic algorithms for current NP-Hard problems.The NSGA-II algorithm adopts a fast, non-dominated sorting approach, which greatly reduces the computational complexity.The crowding distance and crowdedcomparison operator are used to maintain the diversity of the population.The elitist strategy is introduced to expand the sampling space, prevent the loss of the best individual, and improve the operation speed and robustness of the algorithm.Therefore, with the properties of a fast, non-dominated sorting procedure, an elitist strategy, a parameterless approach, and a simple yet efficient constraint-handling method, NSGA-II is more suitable to deal with the current problem [27].However, NSGA-II pays more attention to the quality of the solution and lacks good local search ability, so it is easy to fall into local optimum in the optimization process [28].Aiming at the deficiency of the NSGA-II algorithm, this paper proposes a hybrid multi-objective evolutionary algorithm (NSGA-II-SA) to solve the above model.Based on NSGA-II, this algorithm combines the local search strategy of neighborhood search and the (SA) to generate invasive populations, increase the diversity of populations, and improve the ability of the algorithm to jump out of the optimal local solution.Figure 3 shows the process of the algorithm.
The steps are described as follows: Step 1: Initialize the population, randomly generate the population P t with the size of N g , and calculate the objective function value of each dimension according to the number of objective functions.
Step 2: The population P t is non-dominated sorted and the crowding distance of each individual in the population is calculated.
Step 3: The excellent individuals in population P t are selected by binary tournament mode, and the offspring population P s is obtained by performing uniform two-point crossover and basic bit mutation on them.
Step 4: Merge the parent population P t and offspring population P s , calculate the nondominated level and crowding distance of the mixed population, respectively, eliminate undesirable individuals, and obtain a new population P x with the size of N g .
Step 5: Select some excellent individuals of the new population for local search, and use the local search strategy combining neighborhood search and SA to add the non-dominated solution obtained into the invasive alien population.
Step 6: Merge the invasive alien population with the new population P x , eliminate the undesirable individuals, and obtain the next generation population P n .
Step 7: Judge the current iteration number G and the maximum iteration number G max .If G < G max , go to step 2; otherwise, jump out of the loop and output the Pareto optimal solution set.The steps are described as follows: Step 1: Initialize the population, randomly generate the population Pt with the size of Ng, and calculate the objective function value of each dimension according to the number of objective functions.
Step 2: The population Pt is non-dominated sorted and the crowding distance of each individual in the population is calculated.

Encoding Method
In this paper, natural numbers were used for coding, that is, after task decomposition, the candidate outsourcing services (that is, the candidate outsourcing service providers) corresponding to each subtask are numbered, and the candidate outsourcing service provider code is used as gene loci on chromosomes (Figure 4).
Step 7: Judge the current iteration number G and the maximum iteration number Gmax.If G < Gmax, go to step 2; otherwise, jump out of the loop and output the Pareto optimal solution set.

Encoding Method
In this paper, natural numbers were used for coding, that is, after task decomposition, the candidate outsourcing services (that is, the candidate outsourcing service providers) corresponding to each subtask are numbered, and the candidate outsourcing service provider code is used as gene loci on chromosomes (Figure 4).The encoding sequence represented by the OSCS  4 is [3,2,4,1,4], and then its specific meaning is that the first subtask is assigned to the third outsourcing service provider of the first candidate service set.The second subtask is assigned to the second outsourcing service provider of the second candidate service set.The third subtask is assigned to the fourth outsourcing service provider of the third candidate service set.The fourth subtask is assigned to the first outsourcing service provider of the fourth candidate service set.The fifth subtask is assigned to the fourth outsourcing service provider of the fifth candidate service set.

Local Search Strategy
On the basis of the combination of parent and offspring populations to select the next generation of excellent individuals, this paper adopts the neighborhood search method [29], combined with SA [30], and proposes a local search strategy that integrates the above two to generate an invasive alien population.At the same time, it incorporates the excellent individual set obtained previously, so as to improve the population diversity and prevent the algorithm from falling into the local optimum.
(1) Neighborhood search For candidate outsourcing service provider composition, this paper proposes the corresponding neighborhood structure, and searches the candidate outsourcing service provider composition by randomly selecting point-exchange, two-exchange and three-exchange neighborhood search methods according to the number position of the outsourcing service provider, so as to generate a new candidate outsourcing service provider composition.Among them, point-exchange refers to arbitrarily selecting two number positions in the candidate outsourcing service provider composition and exchanging them.Two-exchange means that two number positions are selected randomly in the candidate outsourcing service provider composition, and then all the number between the two positions are arranged in reverse order.Three-exchange means choosing any three number positions in the candidate outsourcing service provider composition, and then swapping all numbers between the first and second positions with all numbers The encoding sequence represented by the OSCS CS 4 is [3,2,4,1,4], and then its specific meaning is that the first subtask is assigned to the third outsourcing service provider of the first candidate service set.The second subtask is assigned to the second outsourcing service provider of the second candidate service set.The third subtask is assigned to the fourth outsourcing service provider of the third candidate service set.The fourth subtask is assigned to the first outsourcing service provider of the fourth candidate service set.The fifth subtask is assigned to the fourth outsourcing service provider of the fifth candidate service set.

Local Search Strategy
On the basis of the combination of parent and offspring populations to select the next generation of excellent individuals, this paper adopts the neighborhood search method [29], combined with SA [30], and proposes a local search strategy that integrates the above two to generate an invasive alien population.At the same time, it incorporates the excellent individual set obtained previously, so as to improve the population diversity and prevent the algorithm from falling into the local optimum.
(1) Neighborhood search For candidate outsourcing service provider composition, this paper proposes the corresponding neighborhood structure, and searches the candidate outsourcing service provider composition by randomly selecting point-exchange, two-exchange and three-exchange neighborhood search methods according to the number position of the outsourcing service provider, so as to generate a new candidate outsourcing service provider composition.Among them, point-exchange refers to arbitrarily selecting two number positions in the candidate outsourcing service provider composition and exchanging them.Two-exchange means that two number positions are selected randomly in the candidate outsourcing service provider composition, and then all the number between the two positions are arranged in reverse order.Three-exchange means choosing any three number positions in the candidate outsourcing service provider composition, and then swapping all numbers between the first and second positions with all numbers between the second and third positions.Figure 5 shows the neighborhood search methods of point-exchange, two-exchange, and three-exchange.
(2) SA algorithm A large number of studies have shown that SA algorithm can probabilistically jump out of local optimal solutions and eventually converge to the global optimum when solving combinatorial optimization problems.Therefore, SA algorithm is introduced in this paper.However, since SA algorithm can only optimize a single target, and the optimization problem of OCSC is a multi-objective problem, this paper randomly selects one of the objective functions of the problem as the search direction to search for new individuals, and by comparing the objective function value of the old and new solutions, implements the Metropolis criterion [31] to put the obtained excellent individuals into the alien invasion population.between the second and third positions.Figure 5 shows the neighborhood search methods of point-exchange, two-exchange, and three-exchange.(2) SA algorithm A large number of studies have shown that SA algorithm can probabilistically jump out of local optimal solutions and eventually converge to the global optimum when solving combinatorial optimization problems.Therefore, SA algorithm is introduced in this paper.However, since SA algorithm can only optimize a single target, and the optimization problem of OCSC is a multi-objective problem, this paper randomly selects one of the objective functions of the problem as the search direction to search for new individuals, and by comparing the objective function value of the old and new solutions, implements the Metropolis criterion [31] to put the obtained excellent individuals into the alien invasion population.
According to the process of local search in Figure 3, we assume that the original individual before the neighborhood search is a, and its individual fitness (that is, the objective function value) is f(a), and the new individual after the neighborhood search is a', and its individual fitness is f(a').The Metropolis criterion is described as follows: , the new individual is retained according to probability P, that is, when [ ) random 0,1 P > , the new individual a' is retained, otherwise the original individual a is retained.The probability P is shown in formula (15).
In the formula: Tc represents the annealing temperature in the current iteration process, and c represents the number of iterations.

Optimization Strategy
In this paper, the above NSGA-II-SA algorithm is used to solve the proposed composition optimization model, and the Pareto optimal solution set is obtained.It is also necessary to adopt an appropriate optimization strategy to filter the Pareto optimal solution set further, so as to obtain a group of OCSC schemes that best meet users' needs.Therefore, this paper proposes an algorithm result optimization strategy based on the According to the process of local search in Figure 3, we assume that the original individual before the neighborhood search is a, and its individual fitness (that is, the objective function value) is f (a), and the new individual after the neighborhood search is a , and its individual fitness is f (a ).The Metropolis criterion is described as follows: • If f (a ) ≥ f (a), the new individual a' is retained.

•
If f (a ) < f (a), the new individual is retained according to probability P, that is, when P > random[0, 1), the new individual a' is retained, otherwise the original individual a is retained.The probability P is shown in formula (15).
In the formula: T c represents the annealing temperature in the current iteration process, and c represents the number of iterations.

Optimization Strategy
In this paper, the above NSGA-II-SA algorithm is used to solve the proposed composition optimization model, and the Pareto optimal solution set is obtained.It is also necessary to adopt an appropriate optimization strategy to filter the Pareto optimal solution set further, so as to obtain a group of OCSC schemes that best meet users' needs.Therefore, this paper proposes an algorithm result optimization strategy based on the combination of TFNAHP and EWM.This strategy not only considers users' subjective and personalized preferences, but also has strong objectivity and scientificity, which improves the reliability and flexibility of service composition selection to a certain extent.
As the dimensions of each evaluation indicator of outsourcing service providers are different, they are not comparable and cannot be directly calculated.Therefore, this paper adopts the normalization method in literature to normalize all indicators [32].Among them, service time, service cost, and service carbon emission are cost-oriented indicators, and the smaller the value, the more favorable it is.Service reliability is a benefit-oriented indicator, and the larger the value, the more favorable it is.The specific formulas is as follows:

•
For the benefit-oriented indicators, there are: • For the cost-oriented indicators, there are: where f jmax and f jmin are the maximum and minimum values of the candidate OCSC on the jth indicator respectively.f ij is the value of the ith candidate OCSC on the jth indicator, I = 1, 2, . . ., m; j = 1, 2, . . ., n; m is the number of candidate OCSC in Pareto optimal solution set; n is the number of evaluation indicators for candidate OCSC.Therefore, in the Pareto optimal solution set obtained in this paper, the comprehensive evaluation value EV i of the ith candidate OCSC is: where f * i1 , f * i2 , f * i3 and f * i4 are the normalized attribute values of service time, service cost, service carbon emissions, and service reliability, respectively; ST max , SC max , SCE max , SR max , ST min , SC min , SCE min, and SR min are the maximum and minimum values of the candidate outsourcing service composition in terms of service time, service cost, service carbon emission, and service reliability, respectively.ST(i), SC(i), SCE(i), and SR(i) are the attribute values of the ith candidate outsourcing service composition in terms of service time, service cost, service carbon emissions, and service reliability, respectively.ω 1 , ω 2 , ω 3 , and ω 4 are the weight coefficients of the relevant attribute values, respectively, and meet ω 1 + ω 2 + ω 3 + ω 4 = 1.
According to the above Equation (18), it is necessary to assign weights to each evaluation indicator to obtain the comprehensive evaluation value EV i of the candidate OCSC.The corresponding weights of each evaluation indicator can reflect the relative importance of each evaluation indicator in the overall evaluation, as well as the subjective importance that users attach to it, which can greatly influence the selection of the final service composition scheme.Therefore, the selection of the weight allocation method should not only have a certain objective basis, but also reflect users' subjective preferences.In the existing research, weight allocation methods are mainly divided into subjective weighting and objective weighting.Subjective weighting mainly includes the analytic hierarchy process (AHP) [33], expert scoring method [34], etc.Such methods are highly subjective and highly dependent on expert experience.Objective weighting mainly includes Gini coefficient method [35], range method, grey correlation analysis [36], etc.Such methods attach importance to objective data, but ignore expert opinions and user preferences.
In this paper, a comprehensive weight calculation method based on TFNAHP [37] and EWM [38] is proposed by combining the above two weighting methods.The specific steps are shown as follows: (1) TFNAHP weight calculation Step 1: Construct the hierarchical analysis structure model of SCOS (Figure 6).
Step 2: Adopt the 1-9 scale method, as shown in Table 3.The evaluation experts compare and score each indicator of the criterion layer in pairs, and construct the triangular fuzzy judgment matrix B, as shown below.Where n is the number of evaluation indicators, p, q = 1, 2, . . ., n, b pq = l pq , m pq , u pq refers to the importance of indicator p given by the participating experts in relation to another indicator q, which is a triangular fuzzy number.l pq , m pq , u pq , respectively represent the lower, median, and upper bounds of the fuzzy interval b pq .If multiple experts participate in the scoring, the mean value is taken according to the triangular fuzzy number algorithm [39].
Step 4: De-fuzzify the triangular fuzzy number weight D p and D q obtained in the previous step, and calculate the possibility degree V D p ≥ D q of D p ≥ D q and the transformed real number weight d p : 1, m p ≥ m q l q −u p (m p −u p )−(m q −l q ) , m p < m q , u p ≥ l q 0, others (21) d p = minV D p ≥ D q , p = q (22) Step 5: Obtain the standardized weight of each indicator, then the weight coefficient corresponding to the pth indicator of the candidate OCSC is defined as: (2) EWM weight calculation Suppose the number of candidate OCSC schemes in the Pareto optimal solution set is m, and the number of evaluation indicators for each candidate OCSC scheme is n.
Step 2: Normalize the indicator evaluation matrix C according to Equations ( 16) and ( 17), and obtain matrix R = r ij m×n , in which r ij represents the standard value obtained by the normalization of c ij .
Step 3: Calculate the proportion of each evaluation indicator h ij and its information entropy value e j respectively: Step 4: Calculate the weight coefficient of each evaluation indicator, and then the weight coefficient corresponding to the jth indicator of the candidate OCSC is defined as: (3) Determine the comprehensive weight After obtaining the subjective and objective weight vectors, this paper adopts the product sum normalization method to determine the comprehensive weight.Therefore, the comprehensive weight coefficient corresponding to the jth indicator of the candidate OCSC is defined as follows: Based on the above analysis and combined with the application environment of this paper, the candidate OCSC in this paper has four evaluation indicators, so the comprehensive weight coefficient vector W = [ω1, ω2, ω3, ω4].

Example Verification
Since this paper aims to optimize the outsourcing service composition in a lowcarbon environment, even a wrong decision may affect the interests of enterprises and the ecological environment.Based on the case data of a natural gas cylinder head, according to the production and manufacturing process rules of the natural gas cylinder head, the manufacturing tasks are decomposed.In Figure 7, there are five processing items, including rough processing, semi-finishing processing, combination processing of seat ring and pipe, combination processing of diagram shaft cover, and pressure fitting and leakage testing, which are undertaken by different cloud manufacturers and are series structures.After searching and matching through outsourcing cloud platform services, the relevant parameters of the candidate outsourcing service resource set corresponding to each subtask are shown in Table 4, and the logistics time and logistics cost between the candidate outsourcing service providers are shown in Table 5.
OCSC is defined as follows: Based on the above analysis and combined with the application environment of this paper, the candidate OCSC in this paper has four evaluation indicators, so the comprehensive weight coefficient vector W = [ω1, ω2, ω3, ω4].

Example Verification
Since this paper aims to optimize the outsourcing service composition in a low-carbon environment, even a wrong decision may affect the interests of enterprises and the ecological environment.Based on the case data of a natural gas cylinder head, according to the production and manufacturing process rules of the natural gas cylinder head, the manufacturing tasks are decomposed.In Figure 7, there are five processing items, including rough processing, semi-finishing processing, combination processing of seat ring and pipe, combination processing of diagram shaft cover, and pressure fitting and leakage testing, which are undertaken by different cloud manufacturers and are series structures.After searching and matching through outsourcing cloud platform services, the relevant parameters of the candidate outsourcing service resource set corresponding to each subtask are shown in Table 4, and the logistics time and logistics cost between the candidate outsourcing service providers are shown in Table 5.In order to verify the effectiveness of the proposed algorithm in solving the optimization problem of outsourcing service composition under a low-carbon environment, the proposed NSGA-II-SA algorithm, the existing NSGA-II, Pareto Envelope-based Selection Algorithm-II (PESA-II) [40] and Multi-Objective Particle Swarm Optimization (MOPSO) [41] were used to solve the example, respectively.The parameters were set as the initial population N g = 50, the crossover probability P c = 0.9, the mutation probability P m = 0.04, and the maximum number of iterations G max = 100.The proportion of Pareto solutions obtained by each algorithm is plotted as a curve according to the changes presented by different iterations.The proportion of non-dominated solutions of the four algorithms is shown in Figure 8.The number of iterations of the four algorithms to achieve convergence are 23, 26, 19, and 30, respectively, which locates on the same order of magnitude.Figure 9a-d shows the iterative curves of time, cost, carbon emissions, and reliability.In order to verify the effectiveness of the proposed algorithm in solving the optimization problem of outsourcing service composition under a low-carbon environment, the proposed NSGA-II-SA algorithm, the existing NSGA-II, Pareto Envelope-based Selection Algorithm-II (PESA-II) [40] and Multi-Objective Particle Swarm Optimization (MOPSO) [41] were used to solve the example, respectively.The parameters were set as the initial population Ng = 50, the crossover probability Pc = 0.9, the mutation probability Pm = 0.04, and the maximum number of iterations Gmax = 100.The proportion of Pareto solutions obtained by each algorithm is plotted as a curve according to the changes presented by different iterations.The proportion of non-dominated solutions of the four algorithms is shown in Figure 8.The number of iterations of the four algorithms to achieve convergence are 23, 26, 19, and 30, respectively, which locates on the same order of magnitude.Figure 9a-d shows the iterative curves of time, cost, carbon emissions, and reliability.Since NSGA-II-SA is based on NSGA-II algorithm and integrates the local search strategy to solve the same instance under the same parameter settings, it can be seen from Figure 8 that NSGA-II-SA obtains more non-dominated solutions than NSGA-II and other algorithms.It shows that NSGA-II-SA algorithm more easily to jumps out of the local optimal solution, and eventually converges to the global optimal solution, which demonstrates the effectiveness of the local search strategy.Figure 9a-d shows that the algorithm has superior local optimization and global search capabilities, and the average fitness of the NSGA-II-SA algorithm is also optimal in four dimensions, which has better comprehensive performance in the optimization task of service composition in a low-carbon environment.
The Pareto optimal solution set obtained by the NSGA-II-SA algorithm was used as the candidate outsourcing service composition scheme set.There were 257 service composition schemes in total.According to the preferences of the evaluation experts for service time, service cost, service carbon emissions, and service reliability based on company demand, the triangular fuzzy judgment matrix B was constructed, namely:  Since NSGA-II-SA is based on NSGA-II algorithm and integrates the local search strategy to solve the same instance under the same parameter settings, it can be seen from Figure 8 that NSGA-II-SA obtains more non-dominated solutions than NSGA-II and other algorithms.It shows that NSGA-II-SA algorithm more easily to jumps out of the local optimal solution, and eventually converges to the global optimal solution, which demonstrates the effectiveness of the local search strategy.Figure 9a-d shows that the algorithm has superior local optimization and global search capabilities, and the average fitness of the NSGA-II-SA algorithm is also optimal in four dimensions, which has better comprehensive performance in the optimization task of service composition in a low-carbon environment.

Conclusions
CMfg has received strong support from academia and industry in recent years.The cloud service platform can not only meet the complex and unique requirements of users,

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW 3

1 .
Based on the consideration of time, cost, and quality, as well as the influenc carbon emissions and transportation on QoS evaluation indicators, the compos optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which c bines local search strategies of neighborhood search and the Simulated Annea algorithm (SA), a hybrid multi-objective evolutionary algorithm, na Processes 2023, 11, x FOR PEER REVIEW

1 .
Based on the consideration of time, cost, and quality, as well as th carbon emissions and transportation on QoS evaluation indicators, th optimization model of CMfg in a low-carbon environment is establish 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) bines local search strategies of neighborhood search and the Simula algorithm (SA), a hybrid multi-objective evolutionary algor Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW

1 .
Based on the consideration of time, cost, and quality, as well as the carbon emissions and transportation on QoS evaluation indicators, the optimization model of CMfg in a low-carbon environment is establishe 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), bines local search strategies of neighborhood search and the Simulate algorithm (SA), a hybrid multi-objective evolutionary algorit Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic A lated literature of the SCOS problem

Jiang
are two major studies on the service composition account for carbon emissions and tr on QoS evaluation indicators.Secon jective single factor when filtering hensively consider the service com filtering results are biased.Therefo zation model that takes into accoun In addition, an algorithm result op tive is proposed to filter the Paret obtain the relative optimal solution 1. Based on the consideration o carbon emissions and transpo optimization model of CMfg in 2. Based on the Non-dominated bines local search strategies o algorithm (SA), a hybrid Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNS lated literature of the SCOS problem.

Jiang
are two major gaps in curren studies on the service composition and optimal sel account for carbon emissions and transportation, th on QoS evaluation indicators.Secondly, most studi jective single factor when filtering the Pareto opti hensively consider the service composition schem filtering results are biased.Therefore, this paper p zation model that takes into account the effects of In addition, an algorithm result optimization strat tive is proposed to filter the Pareto optimal soluti obtain the relative optimal solution.The contributio 1.Based on the consideration of time, cost, and carbon emissions and transportation on QoS e optimization model of CMfg in a low-carbon e 2. Based on the Non-dominated Sorting Geneti bines local search strategies of neighborhood algorithm (SA), a hybrid multi-objective Genetic Algorithm Jiang, et al.Processes 2023, 11, x FOR PEER REVIEW 3 of 25 Non-dominated Sorting Genetic Algorithm (PoNSGA-III).

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW 3

1 .
Based on the consideration of time, cost, and quality, as well as the influenc carbon emissions and transportation on QoS evaluation indicators, the compos optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which c bines local search strategies of neighborhood search and the Simulated Annea algorithm (SA), a hybrid multi-objective evolutionary algorithm, na Processes 2023, 11, x FOR PEER REVIEW 3 of 25 Non-dominated Sorting Genetic Algorithm (PoNSGA-III).

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW 3 of 25 Non-dominated Sorting Genetic Algorithm (PoNSGA-III).

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).

1 .
Based on the consideration of time, cost, and quality, as well as the carbon emissions and transportation on QoS evaluation indicators, the optimization model of CMfg in a low-carbon environment is establishe 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), bines local search strategies of neighborhood search and the Simulate algorithm (SA), a hybrid multi-objective evolutionary algorit Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic A lated literature of the SCOS problem Que, et al.Chen, et al.Guérout, et al.Zhang, et al.Yang, et al.Liu, et al.The current study However, there are two major studies on the service composition account for carbon emissions and tr on QoS evaluation indicators.Secon jective single factor when filtering hensively consider the service com filtering results are biased.Therefo zation model that takes into accoun In addition, an algorithm result op tive is proposed to filter the Paret obtain the relative optimal solution 1. Based on the consideration o carbon emissions and transpo optimization model of CMfg in 2. Based on the Non-dominated bines local search strategies o algorithm (SA), a hybrid Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNS lated literature of the SCOS problem.

1 .
Based on the consideration of time, cost, and carbon emissions and transportation on QoS e optimization model of CMfg in a low-carbon e 2. Based on the Non-dominated Sorting Geneti bines local search strategies of neighborhood algorithm (SA), a hybrid multi-objective Genetic Algorithm Seghir, et al.Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW 3

1 .
Based on the consideration of time, cost, and quality, as well as the influenc carbon emissions and transportation on QoS evaluation indicators, the compos optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which c bines local search strategies of neighborhood search and the Simulated Annea algorithm (SA), a hybrid multi-objective evolutionary algorithm, na Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).

1 .
Based on the consideration of time, cost, and quality, as well as th carbon emissions and transportation on QoS evaluation indicators, th optimization model of CMfg in a low-carbon environment is establish 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) bines local search strategies of neighborhood search and the Simula algorithm (SA), a hybrid multi-objective evolutionary algor Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).

1 .
Based on the consideration of time, cost, and quality, as well as the carbon emissions and transportation on QoS evaluation indicators, the optimization model of CMfg in a low-carbon environment is establishe 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), bines local search strategies of neighborhood search and the Simulate algorithm (SA), a hybrid multi-objective evolutionary algorit Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic A lated literature of the SCOS problem

Guérout
are two major studies on the service composition account for carbon emissions and tr on QoS evaluation indicators.Secon jective single factor when filtering hensively consider the service com filtering results are biased.Therefo zation model that takes into accoun In addition, an algorithm result op tive is proposed to filter the Paret obtain the relative optimal solution 1. Based on the consideration o carbon emissions and transpo optimization model of CMfg in 2. Based on the Non-dominated bines local search strategies o algorithm (SA), a hybrid Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNS lated literature of the SCOS problem.

1 .
Based on the consideration of time, cost, and carbon emissions and transportation on QoS e optimization model of CMfg in a low-carbon e 2. Based on the Non-dominated Sorting Geneti bines local search strategies of neighborhood algorithm (SA), a hybrid multi-objective Hybrid Genetic Algorithm Jatoth, et al.Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW 3

1 .
Based on the consideration of time, cost, and quality, as well as the influenc carbon emissions and transportation on QoS evaluation indicators, the compos optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which c bines local search strategies of neighborhood search and the Simulated Annea algorithm (SA), a hybrid multi-objective evolutionary algorithm, na Processes 2023, 11, x FOR PEER REVIEW are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su jective single factor when filtering the Pareto optimal solution set, and f hensively consider the service composition scheme from multiple persp filtering results are biased.Therefore, this paper proposes a novel compo zation model that takes into account the effects of carbon emissions and t In addition, an algorithm result optimization strategy combining subject tive is proposed to filter the Pareto optimal solution set from multiple p obtain the relative optimal solution.The contributions of this paper are sum 1.Based on the consideration of time, cost, and quality, as well as th carbon emissions and transportation on QoS evaluation indicators, th optimization model of CMfg in a low-carbon environment is establish 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) bines local search strategies of neighborhood search and the Simula algorithm (SA), a hybrid multi-objective evolutionary algor Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai hensively consider the service composition scheme from multiple perspec filtering results are biased.Therefore, this paper proposes a novel compos zation model that takes into account the effects of carbon emissions and tra In addition, an algorithm result optimization strategy combining subjectiv tive is proposed to filter the Pareto optimal solution set from multiple pe obtain the relative optimal solution.The contributions of this paper are sum 1.Based on the consideration of time, cost, and quality, as well as the carbon emissions and transportation on QoS evaluation indicators, the optimization model of CMfg in a low-carbon environment is establishe 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), bines local search strategies of neighborhood search and the Simulate algorithm (SA), a hybrid multi-objective evolutionary algorit Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic A lated literature of the SCOS problem Seghir, et al.Jatoth, et al.Que, et al.Chen, et al.Guérout, et al.Zhang, et al.Yang, et al.Liu, et al.The current study However, there are two major studies on the service composition account for carbon emissions and tr on QoS evaluation indicators.Secon jective single factor when filtering hensively consider the service com filtering results are biased.Therefo zation model that takes into accoun In addition, an algorithm result op tive is proposed to filter the Paret obtain the relative optimal solution 1. Based on the consideration o carbon emissions and transpo optimization model of CMfg in 2. Based on the Non-dominated bines local search strategies o algorithm (SA), a hybrid Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNS lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS p are two major gaps in curren studies on the service composition and optimal sel account for carbon emissions and transportation, th on QoS evaluation indicators.Secondly, most studi jective single factor when filtering the Pareto opti hensively consider the service composition schem filtering results are biased.Therefore, this paper p zation model that takes into account the effects of In addition, an algorithm result optimization strat tive is proposed to filter the Pareto optimal soluti obtain the relative optimal solution.The contributio 1.Based on the consideration of time, cost, and carbon emissions and transportation on QoS e optimization model of CMfg in a low-carbon e 2. Based on the Non-dominated Sorting Geneti bines local search strategies of neighborhood algorithm (SA), a hybrid multi-objective

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW 3

1 .
Based on the consideration of time, cost, and quality, as well as the influenc carbon emissions and transportation on QoS evaluation indicators, the compos optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which c bines local search strategies of neighborhood search and the Simulated Annea algorithm (SA), a hybrid multi-objective evolutionary algorithm, na Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).
are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su jective single factor when filtering the Pareto optimal solution set, and f hensively consider the service composition scheme from multiple persp filtering results are biased.Therefore, this paper proposes a novel compo zation model that takes into account the effects of carbon emissions and t In addition, an algorithm result optimization strategy combining subject tive is proposed to filter the Pareto optimal solution set from multiple p obtain the relative optimal solution.The contributions of this paper are sum 1.Based on the consideration of time, cost, and quality, as well as th carbon emissions and transportation on QoS evaluation indicators, th optimization model of CMfg in a low-carbon environment is establish 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) bines local search strategies of neighborhood search and the Simula algorithm (SA), a hybrid multi-objective evolutionary algor Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which combines local search strategies of neighborhood search and the Simulated Annealing algorithm (SA), a hybrid multi-objective evolutionary algorithm, named Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).
are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai hensively consider the service composition scheme from multiple perspec filtering results are biased.Therefore, this paper proposes a novel compos zation model that takes into account the effects of carbon emissions and tra In addition, an algorithm result optimization strategy combining subjectiv tive is proposed to filter the Pareto optimal solution set from multiple pe obtain the relative optimal solution.The contributions of this paper are sum 1.Based on the consideration of time, cost, and quality, as well as the carbon emissions and transportation on QoS evaluation indicators, the optimization model of CMfg in a low-carbon environment is establishe 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), bines local search strategies of neighborhood search and the Simulate algorithm (SA), a hybrid multi-objective evolutionary algorit Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic A lated literature of the SCOS problem Que, et al.Chen, et al.Guérout, et al.Zhang, et al.Yang, et al.Liu, et al.The current study However, there are two major studies on the service composition account for carbon emissions and tr on QoS evaluation indicators.Secon jective single factor when filtering hensively consider the service com filtering results are biased.Therefo zation model that takes into accoun In addition, an algorithm result op tive is proposed to filter the Paret obtain the relative optimal solution 1. Based on the consideration o carbon emissions and transpo optimization model of CMfg in 2. Based on the Non-dominated bines local search strategies o algorithm (SA), a hybrid Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorti lated literature of the S

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which com-Processes 2023, 11, x FOR PEER REVIEW 3

1 .
Based on the consideration of time, cost, and quality, as well as the influenc carbon emissions and transportation on QoS evaluation indicators, the compos optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which c Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).
are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su jective single factor when filtering the Pareto optimal solution set, and f hensively consider the service composition scheme from multiple persp filtering results are biased.Therefore, this paper proposes a novel compo zation model that takes into account the effects of carbon emissions and t In addition, an algorithm result optimization strategy combining subject tive is proposed to filter the Pareto optimal solution set from multiple p obtain the relative optimal solution.The contributions of this paper are sum 1.Based on the consideration of time, cost, and quality, as well as th carbon emissions and transportation on QoS evaluation indicators, th optimization model of CMfg in a low-carbon environment is establish 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition optimization model of CMfg in a low-carbon environment is established.2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), which com-Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).
are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai hensively consider the service composition scheme from multiple perspec filtering results are biased.Therefore, this paper proposes a novel compos zation model that takes into account the effects of carbon emissions and tra In addition, an algorithm result optimization strategy combining subjectiv tive is proposed to filter the Pareto optimal solution set from multiple pe obtain the relative optimal solution.The contributions of this paper are sum 1.Based on the consideration of time, cost, and quality, as well as the carbon emissions and transportation on QoS evaluation indicators, the optimization model of CMfg in a low-carbon environment is establishe 2. Based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic A lated literature of the SCOS problem

Jiang
are two major studies on the service composition account for carbon emissions and tr on QoS evaluation indicators.Secon jective single factor when filtering hensively consider the service com filtering results are biased.Therefo zation model that takes into accoun In addition, an algorithm result op tive is proposed to filter the Paret obtain the relative optimal solution 1. Based on the consideration o carbon emissions and transpo optimization model of CMfg in 2. Based on the Non-dominated Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNS lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS p are two major gaps in curren studies on the service composition and optimal sel account for carbon emissions and transportation, th on QoS evaluation indicators.Secondly, most studi jective single factor when filtering the Pareto opti hensively consider the service composition schem filtering results are biased.Therefore, this paper p zation model that takes into account the effects of In addition, an algorithm result optimization strat tive is proposed to filter the Pareto optimal soluti obtain the relative optimal solution.The contributio 1.Based on the consideration of time, cost, and carbon emissions and transportation on QoS e optimization model of CMfg in a low-carbon e 2. Based on the Non-dominated Sorting Geneti Particle Swarm Optimization Guérout, et al.Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition Processes 2023, 11, x FOR PEER REVIEW 3 are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com hensively consider the service composition scheme from multiple perspectives, so filtering results are biased.Therefore, this paper proposes a novel composition opt zation model that takes into account the effects of carbon emissions and transporta In addition, an algorithm result optimization strategy combining subjective and ob tive is proposed to filter the Pareto optimal solution set from multiple perspective obtain the relative optimal solution.The contributions of this paper are summarized 1.Based on the consideration of time, cost, and quality, as well as the influenc carbon emissions and transportation on QoS evaluation indicators, the compos Processes 2023, 11, x FOR PEER REVIEW 3 of 25

1 .
Based on the consideration of time, cost, and quality, as well as the influence of carbon emissions and transportation on QoS evaluation indicators, the composition Processes 2023, 11, x FOR PEER REVIEW 3 of 25 are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai hensively consider the service composition scheme from multiple perspec filtering results are biased.Therefore, this paper proposes a novel compos zation model that takes into account the effects of carbon emissions and tra In addition, an algorithm result optimization strategy combining subjectiv tive is proposed to filter the Pareto optimal solution set from multiple pe obtain the relative optimal solution.The contributions of this paper are sum 1.Based on the consideration of time, cost, and quality, as well as the carbon emissions and transportation on QoS evaluation indicators, the Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Tab lated literature of the SCOS problem.

1 .
Based on the consideration of time, cost, and quality, as carbon emissions and transportation on QoS evaluation ind Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorti lated literature of the S are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su jective single factor when filtering the Pareto optimal solution set, and f hensively consider the service composition scheme from multiple persp filtering results are biased.Therefore, this paper proposes a novel compo zation model that takes into account the effects of carbon emissions and t In addition, an algorithm result optimization strategy combining subject tive is proposed to filter the Pareto optimal solution set from multiple p Processes 2023, 11, x FOR PEER REVIEW 3 of 25 are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai hensively consider the service composition scheme from multiple perspec filtering results are biased.Therefore, this paper proposes a novel compos zation model that takes into account the effects of carbon emissions and tra In addition, an algorithm result optimization strategy combining subjectiv tive is proposed to filter the Pareto optimal solution set from multiple pe Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Tab lated literature of the SCOS problem.

Processes 2023 ,
11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNS lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS p are two major gaps in curren studies on the service composition and optimal sel account for carbon emissions and transportation, th on QoS evaluation indicators.Secondly, most studi jective single factor when filtering the Pareto opti hensively consider the service composition schem filtering results are biased.Therefore, this paper p zation model that takes into account the effects of In addition, an algorithm result optimization strat tive is proposed to filter the Pareto optimal soluti are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su jective single factor when filtering the Pareto optimal solution set, and f hensively consider the service composition scheme from multiple persp filtering results are biased.Therefore, this paper proposes a novel compo zation model that takes into account the effects of carbon emissions and t Processes 2023, 11, x FOR PEER REVIEW 3 of 25 However, there are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to comprehensively consider the service composition scheme from multiple perspectives, so the filtering results are biased.Therefore, this paper proposes a novel composition optimization model that takes into account the effects of carbon emissions and transportation.Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (P lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SC However, there are two major gaps in cu studies on the service composition and optima account for carbon emissions and transportatio on QoS evaluation indicators.Secondly, most s jective single factor when filtering the Pareto hensively consider the service composition sc filtering results are biased.Therefore, this pap zation model that takes into account the effect Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Tab lated literature of the SCOS problem.

Processes 2023 ,
11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNS lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS p However, there are two major gaps in curren studies on the service composition and optimal sel account for carbon emissions and transportation, th on QoS evaluation indicators.Secondly, most studi jective single factor when filtering the Pareto opti hensively consider the service composition schem filtering results are biased.Therefore, this paper p zation model that takes into account the effects of Grey Wolf Optimizer Algorithm Liu, et al.Processes 2023, 11, x FOR PEER REVIEW 3 of 25 are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to compre-Processes 2023, 11, x FOR PEER REVIEW 3 are two major gaps in current research.First, most of the rese studies on the service composition and optimal selection (SCOS) problem have faile account for carbon emissions and transportation, the related constraints, and their ef on QoS evaluation indicators.Secondly, most studies only consider the subjective or jective single factor when filtering the Pareto optimal solution set, and fail to com Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).
are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su jective single factor when filtering the Pareto optimal solution set, and f Processes 2023, 11, x FOR PEER REVIEW 3 of 25 are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or objective single factor when filtering the Pareto optimal solution set, and fail to compre-Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).
are two major gaps in current research.First, most of studies on the service composition and optimal selection (SCOS) problem h account for carbon emissions and transportation, the related constraints, and on QoS evaluation indicators.Secondly, most studies only consider the sub jective single factor when filtering the Pareto optimal solution set, and fai Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic A lated literature of the SCOS problem al. Liu, et al.The current study However, there are two major studies on the service composition account for carbon emissions and tr on QoS evaluation indicators.Secon jective single factor when filtering Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNS lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS p al. Liu, et al.The current study However, there are two major gaps in curren studies on the service composition and optimal sel account for carbon emissions and transportation, th on QoS evaluation indicators.Secondly, most studi jective single factor when filtering the Pareto opti Genetic Algorithm The current study Processes 2023, 11, x FOR PEER REVIEW 3 of 25 are two major gaps in current research.First, most of the research studies on the service composition and optimal selection (SCOS) problem have failed to account for carbon emissions and transportation, the related constraints, and their effects on QoS evaluation indicators.Secondly, most studies only consider the subjective or ob-Non-dominated Sorting Genetic Algorithm (PoNSGA-III).
are two major gaps in current research.First, most o studies on the service composition and optimal selection (SCOS) problem account for carbon emissions and transportation, the related constraints, an on QoS evaluation indicators.Secondly, most studies only consider the su Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).T lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a al.Yang, et al.Liu, et al.The current study However, there are two major gaps in current research.studies on the service composition and optimal selection (SC account for carbon emissions and transportation, the related c on QoS evaluation indicators.Secondly, most studies only co Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (P lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SC al. Yang, et al.Liu, et al.The current study However, there are two major gaps in cu studies on the service composition and optima account for carbon emissions and transportatio on QoS evaluation indicators.Secondly, most s Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic A lated literature of the SCOS problem al. Yang, et al.Liu, et al.The current study However, there are two major studies on the service composition account for carbon emissions and tr on QoS evaluation indicators.Secon Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorti lated literature of the S al. Yang, et al.Liu, et al.

Figure 1 .
Figure 1.CMfg composition optimization mode of a certain type of natural gas cylinder head.

Figure 1 .
Figure 1.CMfg composition optimization mode of a certain type of natural gas cylinder head.

co( 1 )
the jth operation of workpiece i on equipment k P k pq , P k d Actual processing and standby power of the qth process of workpiece p on equipment k T k s , T k e Commencement and completion time of equipment k in this dispatching task α e , α t , α c , α o Carbon emission factors for electrical energy, tool wear, cutting fluid consumption, and lubricating oil consumption w k pq Average tool wear of the qth process of workpiece p on equipment k V k c , T k c Volume of cutting fluid added to equipment k, Cutting fluid replacement cycle of equipment k r k Amount of cutting fluid consumed by equipment k in unit time V k o , T k o Volume of lubricating oil added to equipment k, Lubricating oil replacement cycle of equipment k r k Amount of lubricating oil consumed by equipment k in unit time P s Fixed power of workshop Q r , D r Qualified rate of quality inspection, delivery rate on schedule N q , N s Number of qualified products, total number of production S f , S Number of deliveries on schedule, total number of services Service time ST

Figure 5 .
Figure 5. Neighborhood search methods of point-exchange, two-exchange, and three-exchange.

Figure 5 .
Figure 5. Neighborhood search methods of point-exchange, two-exchange, and three-exchange.

Figure 7 .
Figure 7. Manufacturing process of natural gas cylinder head.

Figure 7 .
Figure 7. Manufacturing process of natural gas cylinder head.

Figure 8 .
Figure 8.Time evolution diagram of the proportion of non-dominated solutions.

Figure 8 .
Figure 8.Time evolution diagram of the proportion of non-dominated solutions.

Figure 9 .
Figure 9. (a) Iteration curves in service time dimension; (b) Iteration curves in service cost dimension; (c) Iteration curves in service carbon emissions dimension; (d) Iteration curves in service cost dimension.

Figure 9 .
Figure 9. (a) Iteration curves in service time dimension; (b) Iteration curves in service cost dimension; (c) Iteration curves in service carbon emissions dimension; (d) Iteration curves in service cost dimension.

Figure 10 .
Figure 10.The transportation route of the CMfg solution.

Figure 10 .
Figure 10.The transportation route of the CMfg solution.
Table 1 summarizes the related literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

QoS Indicators Low-Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting Algorithm Cost Time Quality Bouzary
, et al.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

QoS Indicators Low-Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting A Cost Time Quality
Mix Line Zhang, et al.Flow tion Yang, et al.Grey mize Liu, et al.Ge

Table 1 .
A chronological literature revi

Table 1 .
A chronological literature review on the SCOS p

Table 1
summarizes the related literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summarizes the related literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summarizes the related literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summa lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature revi

Table 1 .
A chronological literature review on the SCOS p

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summ lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summa lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS p

Table 1 .
A chronological literature review on the SCOS problem at a glance.

QoS Indicators Low-Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting Algorithm Cost Time Quality
Genetic AlgorithmSeghir, et al.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature revi

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting Algorithm Cost Time Quality Bouzary
, et al.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summ lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summa lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological Seghir, et al.Jatoth, et al.Que, et al.Chen, et al.Guérout, et al.Zhang, et al.Yang, et al.Liu, et al. zation model that take In addition, an algori tive is proposed to fil obtain the relative opt 1.Based on the con carbon emissions optimization mod 2. Based on the No bines local search algorithm (SA),

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summ lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summa lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature revi

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

QoS Indicators Low-Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting Algorithm Cost Time Quality Bouzary
, et al.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.
Processes 2023, 11, x FOR PEER REVIEW Non-dominated Sorting Genetic Algorithm (PoNSGA-III).Table1summa lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a gla

Table 1 .
A chronological zation model that take In addition, an algori tive is proposed to fil obtain the relative opt 1.Based on the con carbon emissionsNon-dominated Sorting Genetic Algorithm (PoNSGA-III).Table1summarizes the related literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting Algorithm Cost Time Quality Bouzary
, et al.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a gla

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting Algorithm Cost Time Quality Bouzary
, et al.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a gla

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting Algorithm Cost Time Quality Bouzary
, et al.

Table 1
summ lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summa lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 1
summarizes the lated literature of the SCOS problem.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

QoS Indicators Low-Carbon Consideration Transportation Consideration Case Study Subjective and Objective Weighting Algorithm Cost Time Quality Bouzary
, et al.

Table 1 .
A chronological literature review on the SCOS problem at a glance.

Table 2 .
Symbols and their meanings.

Table 4 .
The relevant parameters of the candidate outsourcing service resource set.

Table 4 .
The relevant parameters of the candidate outsourcing service resource set. T

Table 5 .
The logistics time and logistics cost between the candidate outsourcing service providers.