A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling

Abstract

The flexible job shop scheduling problem (FJSP) is significant for realistic manufacturing. However, the job processing time usually is uncertain and changeable during manufacturing. This paper presents a multi-objective FJSP with fuzzy processing time (MOFFJSP) for optimizing the makespan and total machine workload as objectives. To solve the MOFFJSP, a MOEA/D based on reinforcement learning named RMOEA/D is proposed. RMOEA/D can be featured as: (i) an initial strategy with three rules is used to get a high-quality initial population; (ii) a parameter adaption strategy based on Q-learning is proposed to guide the population choose the best parameter to increase diversity; (iii) a variable neighborhood search based on reinforcement learning is designed to lead the solution to choose the right local search method; and (iv) an elite archive is used to improve the usage rate of the abandoned historical solution. RMOEA/D is compared with five well-known realted methods, i.e., MOEA/D, NSGA-II, MOEA/D-M2M, NSGA-III and IAIS on three benchmark suites. The results show that RMOEA/D outperforms these five state-of-art algorithms.