关键词: 分布式混合流水车间调度问题, 装配车间问题, 工人资源, 节能, Q-learning模因算法

Abstract: The distributed hybrid flow shop scheduling problem and assembly shop problem are widely exist in real manufacturing systems.In actual production,besides machine resources,worker resources are also key factors affecting production efficiency.Therefore,the energy-efficient distributed assembly hybrid flow shop scheduling problem was studied considering worker resources.A mixed integer linear programming model was established aiming at minimizing total tardiness and total energy consumption.Based on the characteristics of the problem and its multi-objective nature,a Q-Learning Memetic Algorithm (QLMA) was proposed.In QLMA,to generate excellent initial solutions,a feature-based initialization strategy was introduced.Meanwhile,a Q-learning based variable neighborhood local search was proposed to refine non-dominated solutions,thereby guiding population evolution.Furthermore,an energy-saving strategy was designed to further optimize total energy consumption.Finally,extensive experiments were conducted on 90 large-scale instances,and compared with three other algorithms,the results demonstrated the effectiveness of the proposed algorithm.

Key words: distributed hybrid flow shop scheduling problem, assembly shop problem, worker resources, energy-efficient, Q-learning memetic algorithm