A simulated annealing genetic algorithm based on reinforcement learning for flexible job shop scheduling problem

Yuqing Wang; Dequn Zhao; Hongwei Ma; Suming Zhang; Qianhua Deng

doi:10.1117/12.3011505

7 December 2023 A simulated annealing genetic algorithm based on reinforcement learning for flexible job shop scheduling problem

Yuqing Wang, Dequn Zhao, Hongwei Ma, Suming Zhang, Qianhua Deng

Author Affiliations +

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129410A (2023) https://doi.org/10.1117/12.3011505
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The flexible job-shop scheduling problem surpasses the limitations of conventional workshop scheduling problems that reduce machine constraints, increase uncertainty, and belong to the NP-hard problem. An adaptive simulated annealing genetic algorithm based on reinforcement learning is put forward to overcome the constraints of complex parameter determination and poor local search capabilities that standard genetic algorithms face when dealing with flexible job shop scheduling. The approach uses a multi-parent POX crossover operation and introduces a simulated annealing algorithm into the mutation operation to enhance the method's capability for both global and local optimization in the evolution process. The tournament method is combined with the optimal strategy to ensure the algorithm's convergence. The crossover and mutation parameters are dynamically adjusted and optimized using the reinforcement learning algorithm in conjunction with the improved simulated annealing genetic algorithm so that the parameters of the algorithm can adapt to the evolution process according to experience, and the searchability and computational efficiency of the algorithm are improved. By testing and examining the common examples, the proposed algorithm's rationality and superiority are ultimately demonstrated.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yuqing Wang, Dequn Zhao, Hongwei Ma, Suming Zhang, and Qianhua Deng "A simulated annealing genetic algorithm based on reinforcement learning for flexible job shop scheduling problem", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129410A (7 December 2023); https://doi.org/10.1117/12.3011505

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KEYWORDS

Genetic algorithms

Machine learning

Algorithms

Mathematical optimization

Evolutionary algorithms

Genetics

Algorithm development

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