A Learning-Based Memetic Algorithm for Energy-Efficient Flexible Job-Shop Scheduling With Type-2 Fuzzy Processing Time

Abstract

Green flexible job shop scheduling problem (FJSP) aims to improve profit and reduce energy consumption for modern manufacturing. Meanwhile, FJSP with type-2 fuzzy processing time is proposed to predict the uncertainty in timing constrain for better simulating the practical production. This study addresses the multi-objective energy-efficient flexible job shop scheduling problem with type-2 processing time (ET2FJSP), where the minimization of makespan and total energy consumption are considered simultaneously. The previous studies do not propose the model verification and energy-saving strategy. Moreover, the best parameters required by an algorithm in different stage is different. Therefore, we propose a mixed integer linear programming model and design a learning-based reference vector memetic algorithm (LRVMA). Its main features are: i) four problem-specific initial rules are presented for initialization to generate diverse solutions; ii) four problem-specific local search methods are incorporated to enhance the exploitation; iii) an effective solution selection method depending on the Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; iv) a reinforcement learning-based parameter selection strategy is proposed to improve the diversity of nondominated solutions, and v) an energy-saving strategy is designed to reduce energy consumption. To verify the effectiveness of LRVMA, it is compared against other related algorithms. The results demonstrate that LRVMA outperforms the compared algorithms for solving ET2FJSP.