Combining meta-heuristics and Q-learning for scheduling lot-streaming hybrid flow shops with consistent sublots

Abstract

This study addresses a hybrid flow shop scheduling problem by considering consistent sublots (HFSP_CS) in lot-streaming. The objective is to minimize the maximum completion time (makespan). By mathematically formulating the HFSP_CS, a mathematical model is established. Next, novel combinations of four meta-heuristics and Q-learning-based improvement tactics are proposed for tackling the related problems for the first time. Drawing upon problem-specific characteristics, five local search operators are employed and selected appropriately by utilizing Q-learning throughout the iterations. Furthermore, the model's veracity is demonstrated through the utilization of the CPLEX solver. Then, by resolving 128 instances, the enhanced algorithms showcase their effectiveness. The results show that the artificial bee colony algorithm integrated with Q-learning is the most competitive algorithm among the tested algorithms.