Qianyao Zhu1, Kaizhou Gao1,*, Wuze Huang1, Zhenfang Ma1, Adam Slowik2
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3573-3589, 2024, DOI:10.32604/cmc.2024.055244
Abstract The flow shop scheduling problem is important for the manufacturing industry. Effective flow shop scheduling can bring great benefits to the industry. However, there are few types of research on Distributed Hybrid Flow Shop Problems (DHFSP) by learning assisted meta-heuristics. This work addresses a DHFSP with minimizing the maximum completion time (Makespan). First, a mathematical model is developed for the concerned DHFSP. Second, four Q-learning-assisted meta-heuristics, e.g., genetic algorithm (GA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), and differential evolution (DE), are proposed. According to the nature of DHFSP, six local search operations… More >
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