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Symmetry
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Meta-Heuristics for Manufacturing Systems Optimization, 3rd Edition
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Meta-Heuristics for Manufacturing Systems Optimization, 3rd Edition

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1906

Special Issue Editors

Prof. Dr. Deming Lei

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Guest Editor
School of Automation, Wuhan University of Technology, Wuhan 430062, China
Interests: manufacturing system optimization and scheduling; vehicle routing problem; multi-objective optimization; intelligent optimization; intelligent control
Special Issues, Collections and Topics in MDPI journals
Dr. Jingcao Cai

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Guest Editor
School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, China
Interests: manufacturing system optimization and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Wang

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Guest Editor
Hubei Key Laboratory of Modern Manufacturing and Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China
Interests: machine learning; fuzzy theory; dynamic optimization algorithm; rescheduling

Special Issue Information

Dear Colleagues,

Due to the great success of our Special Issues, ‘Meta-Heuristics for Manufacturing Systems Optimization’ and ‘Meta-Heuristics for Manufacturing Systems Optimization Ⅱ’, we have decided to publish a third edition.

Meta-heuristics are effective tools inspired by the phenomena and behavior of nature and society. There are many meta-heuristics, including genetic algorithms, particle swarm optimization, ant colony optimization, artificial bee colony optimization, the estimation of distribution algorithms, differential evolution, shuffled frog-leaping algorithms, teaching–learning-based optimization, imperialist competitive algorithms, etc. The manufacturing industry is an important part of the economy in a number of countries, including China. Many complicated optimization problems, including scheduling and routing, extensively exist in manufacturing systems. They may have symmetrical features or constraints, and some of them possess asymmetrical conditions that are difficult to tackle using traditional optimization methods. In the last decade, meta-heuristics have become the main path to solve manufacturing system optimization problems, and a number of results have been obtained. 

This Special Issue invites contributions addressing the novel theories, techniques, and applications of meta-heuristic-based manufacturing system optimization. We intend to garner articles on a variety of topics, such as meta-heuristics for multi-objective optimization, meta-heuristics for constrained optimization, multi-objective production scheduling, production scheduling with uncertainty, energy-efficient scheduling, distributed scheduling, dynamic scheduling, etc. Extensive review papers on the latest research findings are also welcome.

Potential topics include, but are not limited to, the following:

  • Meta-heuristics for multi-objective optimization;
  • Meta-heuristics for constrained optimization;
  • Multi-objective production scheduling;
  • Production scheduling with uncertainty;
  • Energy-efficient scheduling;
  • Distributed scheduling;
  • Dynamic scheduling;
  • Machine learning for optimization and scheduling;
  • Meta-heuristics with machine learning for optimization and scheduling;
  • Assembly line balancing;
  • Vehicle routing problem;
  • Optimization problems in semiconductors, irons, automobiles, the chemical industry, etc.

Prof. Dr. Deming Lei
Dr. Jingcao Cai
Dr. Jing Wang
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 2400 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

  • meta-heuristic
  • production scheduling
  • optimization
  • manufacturing systems

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Related Special Issue

Published Papers (3 papers)

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Research

29 pages, 1407 KiB  
Article
Symmetry-Driven Two-Population Collaborative Differential Evolution for Parallel Machine Scheduling in Lace Dyeing with Probabilistic Re-Dyeing Operations
by Jing Wang, Jingsheng Lian, Youpeng Deng, Lang Pan, Huan Xue, Yanming Chen, Debiao Li, Xixing Li and Deming Lei
Symmetry 2025, 17(8), 1243; https://doi.org/10.3390/sym17081243 - 5 Aug 2025
Viewed by 192
Abstract
In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased [...] Read more.
In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization, 3rd Edition)
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28 pages, 12170 KiB  
Article
Research on Multi-Objective Green Vehicle Routing Problem with Time Windows Based on the Improved Non-Dominated Sorting Genetic Algorithm III
by Xixing Li, Chao Gao, Jipeng Wang, Hongtao Tang, Tian Ma and Fenglian Yuan
Symmetry 2025, 17(5), 734; https://doi.org/10.3390/sym17050734 - 9 May 2025
Viewed by 913
Abstract
To advance energy conservation and emissions reduction in urban logistics systems, this study focuses on the green vehicle routing problems with time windows (GVRPTWs), which remains underexplored in balancing environmental and service quality objectives. We propose a comprehensive multi-objective optimization framework that addresses [...] Read more.
To advance energy conservation and emissions reduction in urban logistics systems, this study focuses on the green vehicle routing problems with time windows (GVRPTWs), which remains underexplored in balancing environmental and service quality objectives. We propose a comprehensive multi-objective optimization framework that addresses this gap by simultaneously minimizing total distribution costs and carbon emissions while maximizing customer satisfaction, quantified based on the vehicle’s arrival time at the customer location. The rationale for adopting this tri-objective formulation lies in its ability to reflect real-world trade-offs between economic efficiency, environmental performance, and service level, which are often considered in isolation in previous studies. To tackle this complex problem, we develop an improved Non-Dominated Sorting Genetic Algorithm III (NSGA-III) that incorporates three key enhancements: (1) an integer-encoded initialization method to enhance solution feasibility, (2) a refined selection strategy utilizing crowding distance to maintain population diversity, and (3) an embedded 2-opt local search operator to prevent premature convergence and avoid local optima. Comprehensive validation experiments using Solomon’s benchmark instances and a real-world case demonstrate that the presented algorithm consistently outperforms several state-of-the-art multi-objective optimization methods across key performance metrics. These results highlight the effectiveness and practical relevance of our approach in advancing energy-efficient, low-emission, and customer-centric urban logistics systems. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization, 3rd Edition)
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16 pages, 1161 KiB  
Article
Research on Sliding Mode Control of Robot Fingers Driven by Tendons Based on Nonlinear Disturbance Observer
by Jiufang Pei and Jinshi Cheng
Symmetry 2025, 17(4), 560; https://doi.org/10.3390/sym17040560 - 7 Apr 2025
Cited by 1 | Viewed by 344
Abstract
To reduce weight and improve dexterity performance, dexterous robot fingers usually use tendons for transmission, which may lead to complex nonlinear control problems. In order to improve tracking performance in joint space, this paper proposes an anti-interference controller, which synthesizes the nonsingular fast [...] Read more.
To reduce weight and improve dexterity performance, dexterous robot fingers usually use tendons for transmission, which may lead to complex nonlinear control problems. In order to improve tracking performance in joint space, this paper proposes an anti-interference controller, which synthesizes the nonsingular fast terminal sliding mode technique. A flexible joint dynamic model is established considering the flexibility of the cable-driven mechanism. A nonlinear disturbance observer is adopted to estimate and compensate the system uncertainties and various disturbances, and global fast terminal sliding mode is used to ensure good control performance in both the reaching phase and the sliding mode phase. Furthermore, symmetry is used to simplify dynamic modeling and control design, and the stability of the controller is proven with Lyapunov theory. Finally, the effectiveness of the controller is verified through simulation experiments. The simulation results demonstrate that the proposed controller achieves a steady state in 0.3 s, higher tracking accuracy than the other controllers through quantitative analysis of MAE and MSE metrics, and stronger anti-interference capability, which can satisfy the requirements of finger dexterity operation. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization, 3rd Edition)
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