Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Fu, Yaping; Wang, Yifeng; Gao, Kaizhou; Huang, Min

doi:10.1016/j.compeleceng.2024.109780

Computers and Electrical Engineering

2024

DOI: 10.1016/j.compeleceng.2024.109780

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Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Yaping Fu,

Yifeng Wang,

Kaizhou Gao

et al.

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Cited by 2 publications

References 175 publications

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Low-carbon berth allocation: An analysis of the effectiveness of an enhanced multi-objective artificial bee colony algorithm based on a case study

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Integrated Scheduling of Multi-Objective Job Shops and Material Handling Robots with Reinforcement Learning Guided Meta-Heuristics

Xu,

Jia,

Gao

et al. 2024

Mathematics

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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.

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Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Cited by 2 publications

References 175 publications

Low-carbon berth allocation: An analysis of the effectiveness of an enhanced multi-objective artificial bee colony algorithm based on a case study

Low-carbon berth allocation: An analysis of the effectiveness of an enhanced multi-objective artificial bee colony algorithm based on a case study

Integrated Scheduling of Multi-Objective Job Shops and Material Handling Robots with Reinforcement Learning Guided Meta-Heuristics

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