Integrating meta-heuristics and Q-learning for solving hybrid flow shop scheduling and rescheduling problems with reentrant

The rapid development of the manufacturing industry lead to complex production scheduling processes, in which reentrant operation is an aspect that needs special consideration. Reentrant is rarely considered in the literature, even if it has widespread applications in manufacturing industry. This study investigates the reentrant hybrid flow shop scheduling and rescheduling problems (RHFSRSP) minimize the maximum completion time (makespan). First, a mathematical model is established for the RHFSRSP. Considering the reentrant operation, a left-shift strategy is proposed to optimize the makespan in solution decoding section further. Second, the following four meta-heuristics: genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and artificial bee colony (ABC) are proposed to solve the concerned problems. Furthermore, five local search operations are developed and integrated into the four meta-heuristics to enhance their performance. Third, for selecting high-quality local search operations efficiently, a Q-learning-based local search selection strategy is adopted to replace the random selection strategy. Finally, the relative percentage deviation metric is employed to verify the effectiveness of the proposed enhancement strategies based on a set of benchmark instances. For further performance assessment, the Friedman test is performed on the results of all compared algorithms. The empirical outcomes demonstrate that the ABC algorithm with Q-learning exhibits the strongest competitiveness for solving the RHFSRSP.