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Mathematics
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Mathematics Optimization Algorithms and Scheduling Optimization of Manufacturing Systems
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Mathematics Optimization Algorithms and Scheduling Optimization of Manufacturing Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 5517

Special Issue Editors

Prof. Dr. Zhi Pei

E-Mail Website
Guest Editor
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: scheduling; optimization; queueing system; manufacturing system
Dr. Zhihai Zhang

E-Mail Website
Guest Editor
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
Interests: supply chain management; production and operations management; production planning and scheduling; robust optimization
Dr. Jian Chen

E-Mail Website
Guest Editor
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: scheduling; operations management; smart manufacturing; optimization algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Chaobo Yan

E-Mail Website
Guest Editor
State Key Laboratory for Manufacturing Systems Engineering, Faculty of Electronics and Information, School of Automation Science and Engineering, Xi'an 710049, China
Interests: smart manufacturing; intelligent logistics; autonomous mobile robots

Special Issue Information

Dear Colleagues,

Sustainable production and services can be defined as the production and use of products and services in a manner that is socially beneficial, economically viable and environmentally benign over the whole life cycle. In today’s rapidly changing market environment, it is increasingly necessary for industry leaders to implement a sustainable production and service strategy. There is, therefore, a growing commitment within the global research community to explore novel methods that reduce waste and optimize production processes to advance the development of sustainable business practices.

This Special Issue aims to provide a platform for the discussion and communication of high-quality interdisciplinary studies on research and practice in this emerging field. It will address the interactions between technology, consumption and policy to help identify more sustainable solutions for both production and service systems. This Special Issue focuses on the modelling, analysis, optimization, and control of manufacturing and service systems with sustainable concerns. The advancement of new information technologies, such as the Internet of Things, big data, cloud and edge computing, 5G-enabled manufacturing, digital twins, manufacturing services, and artificial intelligence, will enable more powerful model-based and data-driven methods. We particularly welcome the submission of qualitative and quantitative results from researchers and practitioners. 

The scope of this Special Issue includes, but is not limited to, the following topics:

  1. Energy efficient and environment friendly manufacturing and service systems;
  2. Collaborative robots in sustainable manufacturing systems;
  3. Smart logistics management in sustainable manufacturing and service systems;
  4. Human-machine interaction for sustainable production optimization;
  5. Meta-verse in sustainable manufacturing and services;
  6. Resilience in manufacturing;
  7. Real-time control of sustainable production and service processes;
  8. Data-driven modelling, monitoring and control of sustainable production and service processes;
  9. Service-oriented smart manufacturing and robot as a service (RaaS);
  10. AI-based design and optimization in sustainable production and service system;
  11. Digital twin technology and service-oriented manufacturing technology;
  12. Green production and service operations management;
  13. Sustainable industry and services;
  14. Sustainable supply chain management;
  15. Predictive maintenance for sustainable operations;
  16. Eco-design in product development;
  17. Smart factories and the IoT;
  18. Data-driven decision-making in production and operations;
  19. Robotics and automation for eco-friendly operations;
  20. Advanced process control for sustainable operations.

Prof. Dr. Zhi Pei
Dr. Zhihai Zhang
Dr. Jian Chen
Dr. Chaobo Yan
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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • manufacturing system
  • scheduling
  • service-oriented manufacturing
  • intelligent manufacturing
  • sustainable manufacturing
  • optimization methods
  • optimization algorithms
  • logistics
  • data-driven
  • decision making
  • operations research
  • metaheuristics algorithms
  • industrial applications
  • mathematical programming
  • game theory
  • supply chain
  • multi-objective problems
  • evolutionary computation
  • computational intelligence
  • vehicle routing
  • smart city

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Published Papers (3 papers)

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29 pages, 6039 KB  
Article
A Hierarchical Fractal Space NSGA-II-Based Cloud–Fog Collaborative Optimization Framework for Latency and Energy-Aware Task Offloading in Smart Manufacturing
by Zhiwen Lin, Chuanhai Chen, Jianzhou Chen and Zhifeng Liu
Mathematics 2025, 13(22), 3691; https://doi.org/10.3390/math13223691 - 18 Nov 2025
Viewed by 761
Abstract
The growth of intelligent manufacturing systems has led to a wealth of computation-intensive tasks with complex dependencies. These tasks require an efficient offloading architecture that balances responsiveness and energy efficiency across distributed computing resources. Existing task offloading approaches have fundamental limitations when simultaneously [...] Read more.
The growth of intelligent manufacturing systems has led to a wealth of computation-intensive tasks with complex dependencies. These tasks require an efficient offloading architecture that balances responsiveness and energy efficiency across distributed computing resources. Existing task offloading approaches have fundamental limitations when simultaneously optimizing multiple conflicting objectives while accommodating hierarchical computing architectures and heterogeneous resource capabilities. To address these challenges, this paper presents a cloud–fog hierarchical collaborative computing (CFHCC) framework that features fog cluster mechanisms. These methods enable coordinated, multi-node parallel processing while maintaining data sensitivity constraints. The optimization of task distribution across this three-tier architecture is formulated as a multi-objective problem, minimizing both system latency and energy consumption. To solve this problem, a fractal-based multi-objective optimization algorithm is proposed to efficiently explore Pareto-optimal task allocation strategies by employing recursive space partitioning aligned with the hierarchical computing structure. Simulation experiments across varying task scales demonstrate that the proposed method achieves a 20.28% latency reduction and 3.03% energy savings compared to typical and advanced methods for large-scale task scenarios, while also exhibiting superior solution consistency and convergence. A case study on a digital twin manufacturing system validated its practical effectiveness, with CFHCC outperforming traditional cloud–edge collaborative computing by 12.02% in latency and 11.55% in energy consumption, confirming its suitability for diverse intelligent manufacturing applications. Full article
(This article belongs to the Special Issue Mathematics Optimization Algorithms and Scheduling Optimization of Manufacturing Systems)
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22 pages, 2274 KB  
Article
Real-Time Task Scheduling and Resource Planning for IIoT-Based Flexible Manufacturing with Human–Machine Interaction
by Gahyeon Kwon, Yeongeun Shim, Kyungwoon Cho and Hyokyung Bahn
Mathematics 2025, 13(11), 1842; https://doi.org/10.3390/math13111842 - 31 May 2025
Cited by 4 | Viewed by 2533
Abstract
The emergence of Flexible Manufacturing Systems (FMS) presents new challenges in Industrial IoT (IIoT) environments. Unlike traditional real-time systems, FMS must accommodate task set variability driven by human–machine interaction. As such variations can lead to abrupt resource overload or idleness, a dynamic scheduling [...] Read more.
The emergence of Flexible Manufacturing Systems (FMS) presents new challenges in Industrial IoT (IIoT) environments. Unlike traditional real-time systems, FMS must accommodate task set variability driven by human–machine interaction. As such variations can lead to abrupt resource overload or idleness, a dynamic scheduling mechanism is required. Although prior studies have explored dynamic scheduling, they often relax deadlines for lower-criticality tasks, which is not well suited to IIoT systems with strict deadline constraints. In this paper, instead of treating dynamic scheduling as a prediction problem, we model it as deterministic planning in response to explicit, observable user input. To this end, we precompute feasible resource plans for anticipated task set variations through offline optimization and switch to the appropriate plan at runtime. During this process, our approach jointly optimizes processor speeds, memory allocations, and edge/cloud offloading decisions, which are mutually interdependent. Simulation results show that the proposed framework achieves up to 73.1% energy savings compared to a baseline system, 100% deadline compliance for real-time production tasks, and low-latency responsiveness for user-interaction tasks. We anticipate that the proposed framework will contribute to the design of efficient, adaptive, and sustainable manufacturing systems. Full article
(This article belongs to the Special Issue Mathematics Optimization Algorithms and Scheduling Optimization of Manufacturing Systems)
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26 pages, 2688 KB  
Article
Modeling and Optimization of Cable Production Scheduling by Incorporating an Ant Colony Algorithm
by Changbiao Zhu, Chongxin Wang, Zhonghua Ni, Xiaojun Liu and Abbas Raza
Mathematics 2025, 13(8), 1235; https://doi.org/10.3390/math13081235 - 9 Apr 2025
Cited by 2 | Viewed by 1587
Abstract
With the development of small batch and multi-batch service production mode, manual scheduling by hand has been difficult to adapt to the production of a large number of complex orders. This work proposed a cable production scheduling optimization method based on an ant [...] Read more.
With the development of small batch and multi-batch service production mode, manual scheduling by hand has been difficult to adapt to the production of a large number of complex orders. This work proposed a cable production scheduling optimization method based on an ant colony algorithm, aiming at solving the problems of the inefficiency and underutilization of resources in the process of traditional cable scheduling. Applying an ant colony (ACO) algorithm to solve the production scheduling problem achieved the intelligent scheduling and optimization of production tasks. The method utilizes the search and optimization capabilities of the ant colony algorithm, with the characteristics of the cable production line, achieving a reasonable allocation and scheduling of production tasks. After applying the proposed model to the cable production line, the scheduling scheme generated by the ACO algorithm-based objective order scheduling method reduced the total production time required from 3 days to 2.6882 days, resulting in a 10.04% increase in production efficiency. The results show that the method can effectively improve the production efficiency and resource utilization of the cable production line, and has high practicality and feasibility. Full article
(This article belongs to the Special Issue Mathematics Optimization Algorithms and Scheduling Optimization of Manufacturing Systems)
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