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Knowledge- and Learning-Driven Meta-Heuristics for Addressing Complex Optimization and Scheduling...
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Knowledge- and Learning-Driven Meta-Heuristics for Addressing Complex Optimization and Scheduling Problems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 3354

Special Issue Editors

Prof. Dr. Yaping Fu

E-Mail Website
Guest Editor
Department of Management Science and Engineering, Qingdao University, Qingdao, China
Interests: production planning and scheduling; evolutionary multi-objective optimization; simulation optimization; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Dr. Kaizhou Gao

E-Mail Website
Guest Editor
Department of Engineering Science, Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau, China
Interests: artificial intelligence; intelligent optimization theory, methods and applications; reinforcement learning; complex systems modeling, optimization and scheduling; intelligent transportation; intelligent manufacturing; smart city
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Naiqi Wu

E-Mail Website
Guest Editor
Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau, China
Interests: intelligent manufacturing; discrete event systems, and petri net theory and applications; production planning, scheduling and control; intelligent logistics and transportation; energy systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ponnuthurai Nagaratnam Suganthan

E-Mail Website
Guest Editor
KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar
Interests: pattern recognition; soft computing; evolutionary computation; multiobjective optimization; optimization algorithms; classification; advanced machine learning algorithms

Special Issue Information

Dear Colleagues,

In the domain of addressing intricate optimization and scheduling challenges across a variety of industry systems, knowledge- and learning-driven meta-heuristics, e.g., genetic algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), and artificial bee colony (ABC), have emerged as powerful tools, offering unparalleled adaptability and computational prowess. This Special Issue delves into the frontier of combining meta-heuristics with problem-specific knowledge and machine learning techniques to cope with the modeling, optimization, and scheduling of engineering optimization problems. Our objective is to explore the latest advancements in meta-heuristics and ensemble methodologies integrated with problem-specific knowledge and machine learning, with a particular focus on their innovative applications in addressing a wide range of optimization and scheduling challenges.

The potential topics include (but are not limited to):

  • Swarm intelligence and evolutionary algorithms, e.g., GA, PSO, DE, and ABC, for engineering optimization problems;
  • Advanced multi-objective and multi-task optimization with meta-heuristic algorithms;
  • Dynamic optimization and adaptive meta-heuristic algorithms;
  • Large-scale distributed scheduling and hybrid scheduling according to meta-heuristics;
  • Designs of knowledge- and learning-based meta-heuristics for various continuous optimizations;
  • Integration of problem knowledge, machine learning, and meta-heuristics in domain-specific applications
    • Production scheduling;
    • Energy-efficiency scheduling;
    • Heath care center scheduling and routing optimization;
    • Traffic signal control, optimization, and scheduling;
    • Vehicle routing problems;
    • Port planning and scheduling;
    • Unmanned vehicles/unmanned surface vessels task assignment and routing planning;
    • Project, grid/cloud, and smart city/building scheduling;
    • Sustainability and green scheduling;
    • Emerging real-world combinatorial optimization.

Prof. Dr. Yaping Fu
Dr. Kaizhou Gao
Prof. Dr. Naiqi Wu
Prof. Dr. Ponnuthurai Nagaratnam Suganthan
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. 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

  • swarm intelligence
  • evolutionary algorithms
  • multi-objective and multi-task optimization
  • meta-heuristic algorithms
  • dynamic optimization
  • machine learning
  • production scheduling
  • energy-efficiency scheduling
  • heath care center scheduling and routing optimization
  • traffic signal control, optimization, and scheduling
  • vehicle routing problems
  • port planning and scheduling
  • unmanned vehicles/unmanned surface vessels task assignment and routing planning
  • project, grid/cloud, and smart city/building scheduling
  • sustainability and green scheduling
  • emerging real-world combinatorial optimization

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Published Papers (4 papers)

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Research

23 pages, 860 KiB  
Article
Hybrid Disassembly Line Balancing of Multi-Factory Remanufacturing Process Considering Workers with Government Benefits
by Xiaoyu Niu, Xiwang Guo, Peisheng Liu, Jiacun Wang, Shujin Qin, Liang Qi, Bin Hu and Yingjun Ji
Mathematics 2025, 13(5), 880; https://doi.org/10.3390/math13050880 - 6 Mar 2025
Viewed by 433
Abstract
Optimizing multi-factory remanufacturing systems with social welfare considerations presents critical challenges in task allocation and process coordination. This study addresses this gap by proposing a hybrid disassembly line balancing and multi-factory remanufacturing process optimization problem, considering workers with government benefits. A mixed-integer programming [...] Read more.
Optimizing multi-factory remanufacturing systems with social welfare considerations presents critical challenges in task allocation and process coordination. This study addresses this gap by proposing a hybrid disassembly line balancing and multi-factory remanufacturing process optimization problem, considering workers with government benefits. A mixed-integer programming model is formulated to maximize profit, and its correctness is verified using the CPLEX solver. Furthermore, a discrete zebra optimization algorithm is proposed to solve the model, integrating a survival-of-the-fittest strategy to improve its optimization capabilities. The effectiveness and convergence of the algorithm are demonstrated through experiments on disassembly cases, with comparisons made to six peer algorithms and CPLEX. The experimental results highlight the importance of this research in improving resource utilization efficiency, reducing environmental impacts, and promoting sustainable development. Full article
(This article belongs to the Special Issue Knowledge- and Learning-Driven Meta-Heuristics for Addressing Complex Optimization and Scheduling Problems)
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23 pages, 1857 KiB  
Article
An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems
by Xinshuo Cui, Qingbo Meng, Jiacun Wang, Xiwang Guo, Peisheng Liu, Liang Qi, Shujin Qin, Yingjun Ji and Bin Hu
Mathematics 2025, 13(2), 256; https://doi.org/10.3390/math13020256 - 14 Jan 2025
Viewed by 734
Abstract
In order to protect the environment, an increasing number of people are paying attention to the recycling and remanufacturing of EOL (End-of-Life) products. Furthermore, many companies aim to establish their own closed-loop supply chains, encouraging the integration of disassembly and assembly lines into [...] Read more.
In order to protect the environment, an increasing number of people are paying attention to the recycling and remanufacturing of EOL (End-of-Life) products. Furthermore, many companies aim to establish their own closed-loop supply chains, encouraging the integration of disassembly and assembly lines into a unified closed-loop production system. In this work, a hybrid production line that combines disassembly and assembly processes, incorporating human–machine collaboration, is designed based on the traditional disassembly line. A mathematical model is proposed to address the human–machine collaboration disassembly and assembly hybrid line balancing problem in this layout. To solve the model, an evolutionary learning-based whale optimization algorithm is developed. The experimental results show that the proposed algorithm is significantly faster than CPLEX, particularly for large-scale disassembly instances. Moreover, it outperforms CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while maintaining high solution quality. Full article
(This article belongs to the Special Issue Knowledge- and Learning-Driven Meta-Heuristics for Addressing Complex Optimization and Scheduling Problems)
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28 pages, 12012 KiB  
Article
Integrated Scheduling of Multi-Objective Job Shops and Material Handling Robots with Reinforcement Learning Guided Meta-Heuristics
by Zhangying Xu, Qi Jia, Kaizhou Gao, Yaping Fu, Li Yin and Qiangqiang Sun
Mathematics 2025, 13(1), 102; https://doi.org/10.3390/math13010102 - 30 Dec 2024
Viewed by 752
Abstract
This study investigates the integrated multi-objective scheduling problems of job shops and material handling robots (MHR) with minimising the maximum completion time (makespan), earliness or tardiness, and total energy consumption. The collaborative scheduling of MHR and machines can enhance efficiency and reduce costs. [...] Read more.
This study investigates the integrated multi-objective scheduling problems of job shops and material handling robots (MHR) with minimising the maximum completion time (makespan), earliness or tardiness, and total energy consumption. The collaborative scheduling of MHR and machines can enhance efficiency and reduce costs. First, a mathematical model is constructed to articulate the concerned problems. Second, three meta-heuristics, i.e., genetic algorithm (GA), differential evolution, and harmony search, are employed, and their variants with seven local search operators are devised to enhance solution quality. Then, reinforcement learning algorithms, i.e., Q-learning and state–action–reward–state–action (SARSA), are utilised to select suitable local search operators during iterations. Three reward setting strategies are designed for reinforcement learning algorithms. Finally, the proposed algorithms are examined by solving 82 benchmark instances. Based on the solutions and their analysis, we conclude that the proposed GA integrating SARSA with the first reward setting strategy is the most competitive one among 27 compared algorithms. Full article
(This article belongs to the Special Issue Knowledge- and Learning-Driven Meta-Heuristics for Addressing Complex Optimization and Scheduling Problems)
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20 pages, 1598 KiB  
Article
Large Language Model-Assisted Reinforcement Learning for Hybrid Disassembly Line Problem
by Xiwang Guo, Chi Jiao, Peng Ji, Jiacun Wang, Shujin Qin, Bin Hu, Liang Qi and Xianming Lang
Mathematics 2024, 12(24), 4000; https://doi.org/10.3390/math12244000 - 19 Dec 2024
Viewed by 1063
Abstract
Recycling end-of-life products is essential for reducing environmental impact and promoting resource reuse. In the realm of remanufacturing, researchers are increasingly concentrating on the challenge of the disassembly line balancing problem (DLBP), particularly on how to allocate work tasks effectively to enhance productivity. [...] Read more.
Recycling end-of-life products is essential for reducing environmental impact and promoting resource reuse. In the realm of remanufacturing, researchers are increasingly concentrating on the challenge of the disassembly line balancing problem (DLBP), particularly on how to allocate work tasks effectively to enhance productivity. However, many current studies overlook two key issues: (1) how to reasonably arrange the posture of workers during disassembly, and (2) how to reasonably arrange disassembly tasks when the disassembly environment is not a single type of disassembly line but a hybrid disassembly line. To address these issues, we propose a mixed-integrated programming model suitable for linear and U-shaped hybrid disassembly lines, while also considering how to reasonably allocate worker postures to alleviate worker fatigue. Additionally, we introduce large language model-assisted reinforcement learning to solve this model, which employs a Dueling Deep Q-Network (Duel-DQN) to tackle the problem and integrates a large language model (LLM) into the algorithm. The experimental results show that compared to solutions that solely use reinforcement learning, large language model-assisted reinforcement learning reduces the number of iterations required for convergence by approximately 50% while ensuring the quality of the solutions. This provides new insights into the application of LLM in reinforcement learning and DLBP. Full article
(This article belongs to the Special Issue Knowledge- and Learning-Driven Meta-Heuristics for Addressing Complex Optimization and Scheduling Problems)
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