Learning-Assisted Optimization Methods for Shop Scheduling Problems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D: Statistics and Operational Research".
Deadline for manuscript submissions: 23 May 2027 | Viewed by 28
Editors
Interests: green manufacturing; mathematical modeling; optimization algorithm; production scheduling
Interests: intelligent manufacturing system; digital modeling and simulation; digital twin in manufacturing operations; production line balancing; smart optimization algorithms
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Shop scheduling problems (SSP) include single machine, parallel machine, flow shop, flexible flow shop, job shop, flexible job shop, open shop and flexible open shop scheduling problems and their extended problems. They widely exist in manufacturing industries. Learning-assisted optimization methods refer to the integration of machine learning (ML) techniques with optimization methods, aiming to enhance optimization processes. In recent years, many researchers incorporated ML into optimization methods, such as mathematical modeling method, meta-heuristic and matheuristic algorithms. The fusion of ML and optimization methods has gained extensive attention from both academia and industry, demonstrating good performance in shop scheduling optimization problems.
The main aim of this Special Issue is to report on the latest advancements in learning-assisted optimization methods for solving SSP, covering theories, algorithms and applications. We expect submitted papers to delve into how to design learning strategies and optimization methods by incorporating SSP characteristics, thereby achieving good solutions more effectively and efficiently. We encourage the submission of original research articles and comprehensive review papers in this Special Issue, including, but not limited to, the following topics:
- Mixed integer linear programming or constraint programming modeling for SSP
- Deep reinforcement learning or large language model for SSP
- Metaheuristic or matheuristic algorithms for SSP
- Deep reinforcement learning-assisted mixed integer linear programming or constraint programming modeling for SSP
- Deep reinforcement learning-assisted metaheuristic or matheuristic algorithms for SSP
- Large language model-assisted mixed integer linear programming or constraint programming modeling for SSP
- Large language model-assisted metaheuristic or matheuristic algorithms for SSP
- Large language model-assisted deep reinforcement learning methods for SSP
We look forward to receiving your contributions.
Dr. Leilei Meng
Dr. Lei Yue
Guest Editors
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Keywords
- shop scheduling problem
- optimization
- mixed integer linear programming
- constraint programming
- deep reinforcement learning
- large language model
- metaheuristic algorithm
- matheuristic algorithm
- machine learning
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