A Dual-Population Genetic Algorithm with Q-Learning for Multi-Objective Distributed Hybrid Flow Shop Scheduling Problem

In real-world production processes, the same enterprise often has multiple factories or one factory lines, and objectives need to be considered in process. A dual-population genetic algorithm with Q-learning is proposed minimize maximum completion time number of tardy jobs for distributed hybrid flow shop scheduling problems, which have some symmetries machines. Multiple crossover mutation operators are proposed, only search strategy combination, including operator operator, selected each iteration. population assessment method provided evaluate evolutionary state at initial after Two populations adopt different strategies, best first second under guidance Q-learning. Experimental results show that competitive solving multi-objective problems.