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Green Scheduling Optimization in Manufacturing Systems- Application of Intelligent Optimization...
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Special Issue "Green Scheduling Optimization in Manufacturing Systems- Application of Intelligent Optimization Algorithms with Symmetry/Asymmetry"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: 15 May 2023 | Viewed by 4467

Special Issue Editors

Dr. Chao Lu
E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan, China
Interests: multi-objective evolutionary algorithms and shop scheduling problems
Dr. Biao Zhang

E-Mail Website
Guest Editor
School of Computer Science, Liaocheng University, Shandong, China
Interests: multi-objective evolutionary algorithms and shop scheduling problems
Dr. Jin Yi
E-Mail Website
Guest Editor
School of Mechanical and Vehicle Engineering, Chongqing University, Chonging, China
Interests: evolutionary algorithms, surrogate-assisted global optimization

Special Issue Information

Dear Colleagues,

Green (or low-carbon) manufacturing has recently become a hot topic in both academia and industry because of global warming and the greenhouse effect. Among manufacturing systems, shop scheduling plays a key role in saving energy and reducing emissions. Shop scheduling problems in manufacturing systems need to consider both production benefits and environmental objectives, which are symmetrical and equally important. However, these types of problems are difficult to solve using traditional optimization methods, especially as they may have symmetrical characteristics or constraints, and some have asymmetrical conditions which increase the difficulty of solving them. Metaheuristics are effective methods to address such problems. This Special Issue is devoted to collecting new, original results in this field together with applications to real-life situations. Potential topics include but are not limited to intelligent optimization algorithms for sustainable manufacturing or green scheduling in various shops, dynamic shop scheduling, energy-efficient shop scheduling, low-carbon shop scheduling, and optimization problems in semiconductors, iron, automobile, chemical industry, etc.

Dr. Chao Lu
Dr. Biao Zhang
Dr. Jin Yi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent optimization algorithms
  • multi-objective
  • scheduling
  • energy-efficient
  • low-carbon
  • intelligent manufacturing
  • green logistics and supply chain
  • surrogate models

Published Papers (4 papers)

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Article
A Chaotic Genetic Algorithm with Variable Neighborhood Search for Solving Time-Dependent Green VRPTW with Fuzzy Demand
by
Hao Fan
,
Xiaoxue Ren
,
Yueguang Zhang
,
Zimo Zhen
and
Houming Fan
Symmetry 2022, 14(10), 2115; https://doi.org/10.3390/sym14102115 - 12 Oct 2022
Viewed by 511
Abstract
Aiming at the time-dependent green vehicle routing problem with fuzzy demand, this paper comprehensively considers the dispatch costs, time window penalty costs, fuel costs, and the effects of vehicle travel speed, road gradient, and vehicle load on fuel consumption, a mixed integer programming [...] Read more.
Aiming at the time-dependent green vehicle routing problem with fuzzy demand, this paper comprehensively considers the dispatch costs, time window penalty costs, fuel costs, and the effects of vehicle travel speed, road gradient, and vehicle load on fuel consumption, a mixed integer programming model is formulated based on pre-optimization and re-optimization strategies. The traditional vehicle routing problems are modeled based on a symmetric graph. In this paper, considering the influence of time-dependent networks on route optimization, modeling is based on an asymmetric graph, which increases the complexity of the problem. In the pre-optimization stage, a pre-optimization scheme is generated based on the credibility measure theory; in the re-optimization stage, a new re-optimization strategy was used to deal with the service failure node In order to solve this problem, we developed a chaotic genetic algorithm with variable neighborhood search, pseudo-randomness of chaos was introduced to ensure the diversity of initial solutions, and adaptive neighborhood search times strategy and inferior solution acceptance mechanism were proposed to improve the performance of the algorithm. The numerical results show that the model and algorithm we proposed are effective. Full article
(This article belongs to the Special Issue Green Scheduling Optimization in Manufacturing Systems- Application of Intelligent Optimization Algorithms with Symmetry/Asymmetry)
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Article
A Matheuristic Approach for the No-Wait Flowshop Scheduling Problem with Makespan Criterion
by
Yu Gao
,
Ziyue Wang
,
Liang Gao
and
Xinyu Li
Symmetry 2022, 14(5), 913; https://doi.org/10.3390/sym14050913 - 29 Apr 2022
Cited by 1 | Viewed by 803
Abstract
The No-wait Flowshop Scheduling Problem (NWFSP) has always been a research hotspot because of its importance in various industries. This paper uses a matheuristic approach that combines exact and heuristic algorithms to solve it with the objective to minimize the makespan. Firstly, according [...] Read more.
The No-wait Flowshop Scheduling Problem (NWFSP) has always been a research hotspot because of its importance in various industries. This paper uses a matheuristic approach that combines exact and heuristic algorithms to solve it with the objective to minimize the makespan. Firstly, according to the symmetry characteristics in NWFSP, a local search method is designed, where the first job and the last job in the symmetrical position remain unchanged, and then, a three-level neighborhood division method and the corresponding rapid evaluation method at each level are given. The two proposed heuristic algorithms are built on them, which can effectively avoid al-ready searched areas, so as to quickly obtain the local optimal solutions, and even directly obtain the optimal solutions for small-scale instances. Secondly, using the equivalence of this problem and the Asymmetric Traveling Salesman Problem (ATSP), an exact method for solving NWFSP is constructed. Importing the results of the heuristics into the model, the efficiency of the Mil-ler-Tucker-Zemlin (MTZ) model for solving small-scale NWFSP can be improved. Thirdly, the matheuristic algorithm is used to test 141 instances of the Tailard and Reeves benchmarks, and each optimal solution can be obtained within 134 s, which verifies the stability and effectiveness of the algorithm. Full article
(This article belongs to the Special Issue Green Scheduling Optimization in Manufacturing Systems- Application of Intelligent Optimization Algorithms with Symmetry/Asymmetry)
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Article
A Multi-Objective Cellular Memetic Optimization Algorithm for Green Scheduling in Flexible Job Shops
by
Yong Wang
,
Wange Peng
,
Chao Lu
and
Huan Xia
Symmetry 2022, 14(4), 832; https://doi.org/10.3390/sym14040832 - 18 Apr 2022
Cited by 2 | Viewed by 1047
Abstract
In the last 30 years, a flexible job shop scheduling problem (FJSP) has been extensively explored. Production efficiency is a widely utilized objective. With the rise in environmental awareness, green objectives (e.g., energy consumption) have received a lot of attention. Nevertheless, energy consumption [...] Read more.
In the last 30 years, a flexible job shop scheduling problem (FJSP) has been extensively explored. Production efficiency is a widely utilized objective. With the rise in environmental awareness, green objectives (e.g., energy consumption) have received a lot of attention. Nevertheless, energy consumption has received little attention. Furthermore, controllable processing times (CPT) should be considered in the field of scheduling, because they are closer to some real production. Therefore, this work investigates a FJSP with CPT (i.e., FJSP-CPT) where asymmetrical conditions and symmetrical constraints increase the difficulty of problem solving. The objectives of FJSP-CPT are to minimize simultaneously the maximum completion time (i.e., makespan) and total energy consumption (TEC). First of all, a mathematical model of this multi-objective FJSP-CPT was formulated. To optimize this problem, a novel multi-objective cellular memetic optimization algorithm (MOCMOA) was presented. The proposed MOMOA combined the advantages of cellular structure for global exploration and variable neighborhood search (VNS) for local exploitation. At last, MOCMOA was compared against other multi-objective optimization approaches by performing experiments. Numerical experiments reveal that the presented MOCMOA is superior to its competitors in 15 instances regarding three commonly used performance metrics. Full article
(This article belongs to the Special Issue Green Scheduling Optimization in Manufacturing Systems- Application of Intelligent Optimization Algorithms with Symmetry/Asymmetry)
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Article
Energy Saving in Single-Machine Scheduling Management: An Improved Multi-Objective Model Based on Discrete Artificial Bee Colony Algorithm
by
Jing Jia
,
Chao Lu
and
Lvjiang Yin
Symmetry 2022, 14(3), 561; https://doi.org/10.3390/sym14030561 - 11 Mar 2022
Cited by 1 | Viewed by 1077
Abstract
Green manufacturing, which takes environmental effect and production benefit into consideration, has attracted increasing concern with the target of carbon peaking and carbon neutrality proposed. As a critical process in the manufacturing system, shop scheduling is also an important method for enterprises to [...] Read more.
Green manufacturing, which takes environmental effect and production benefit into consideration, has attracted increasing concern with the target of carbon peaking and carbon neutrality proposed. As a critical process in the manufacturing system, shop scheduling is also an important method for enterprises to achieve green manufacturing. Therefore, it is necessary to consider both production benefits and environmental objectives in shop scheduling, which are symmetrical and equally important. In addition, noise pollution has become an important environmental issue that cannot be ignored in the manufacturing processes, but which has been paid less attention in previous studies. Thus, the MODABC algorithm, with the optimization objectives of simultaneously minimizing lead-time/tardiness cost and job-shop noise pollution emission is proposed in this paper. We designed a discrete permutation-based two-layer encoding mechanism to generate the initial population. Then, three crossover methods were used to perform nectar update operations in the employed bee search phase, and three neighbourhood structures were used to improve the onlooker bee search operations. Finally, the MODABC algorithm was compared with other classical MOEAs. The results demonstrate that MODABC can provide non-dominated solution set with good convergence and distribution, and show significant superiority in solving green single-machine multi-objective scheduling problems. Full article
(This article belongs to the Special Issue Green Scheduling Optimization in Manufacturing Systems- Application of Intelligent Optimization Algorithms with Symmetry/Asymmetry)
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