A Q-learning-driven genetic algorithm for the distributed hybrid flow shop group scheduling problem with delivery time windows

Abstract

The distributed cell manufacturing can leverage resources from different geographic locations to achieve more efficient production and services. In its production lines, jobs requiring setup conditions are grouped together. To improve the flexibility of the production process, each process consists of multiple processing stages with each stage containing one or more parallel machines, and at least one stage has two or more than two machines. This shop floor layout can balance the workload of the individual machines and expand production capacity. In addition, on-time delivery is a significant criterion for assessing the impact on the competitiveness and long-term development of an organization. In this context, we study the distributed hybrid flow shop group scheduling problem (DHFGSP) with the total weighted earliness and tardiness criterion. For the first time, we establish a mixed integer linear programming model of DHFGSP, and validate its accuracy through the Gurobi solver. Meanwhile, we design a Q-learning-driven genetic algorithm (QGA) to solve the above problem. Within QGA, we first propose an idle-time insertion method for the last stage to further minimize the operation objective. Then, we devise multiple neighborhood structures tailored to penalty groups and worst factories, integrating them into three variable neighborhood searches as mutation methods. Next, a Q-learning table is designed by incorporating two states and eight actions, each action representing a unique combination of crossover and mutation techniques. The modified design can make the population into an intelligent agent, autonomously selecting evolutionary actions. Through experimental results and analysis on 405 test instances, we validate the effectiveness of all proposed strategies and confirm that QGA outperforms other existing advanced algorithms in solving the DHFGSP.