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

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 constraint for better simulating the practical production. This study addresses the multiobjective energy-efficient FJSP 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 are different. Therefore, we propose a mixed-integer linear programming model and design a learning-based reference vector memetic algorithm (LRVMA). Its main features are: 1) four problem-specific initial rules that are presented for initialization to generate diverse solutions; 2) four problem-specific local search methods that are incorporated to enhance the exploitation; 3) an effective solution selection method depending on the Tchebycheff decomposition strategy that is utilized to balance the convergence and diversity; 4) a reinforcement learning-based parameter selection strategy that is proposed to improve the diversity of nondominated solutions; and 5) an energy-saving strategy that 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.

Automated summary

This study proposes a mixed-integer linear programming model and a learning-based reference vector memetic algorithm (LRVMA) to solve the multiobjective energy-efficient flexible job-shop scheduling problem (FJSP) with type-2 fuzzy processing time (ET2FJSP). LRVMA includes specific initial rules, local search methods, a solution selection method based on Tchebycheff decomposition strategy, a reinforcement learning-based parameter selection strategy, and an energy-saving strategy. Experimental results show that LRVMA outperforms other algorithms for solving ET2FJSP.