A decomposition-based memetic algorithm to solve the biobjective green flexible job shop scheduling problem with interval type-2 fuzzy processing time

With increasing environmental awareness, energy consumption of industries is becoming a popular research topic. In industrial manufacturing, processing time is highly uncertain. This study investigates a green flexible job shop scheduling problem with interval type-2 fuzzy processing time (IT2GFJSP) with the objectives of makespan and energy consumption. To solve this problem, a decomposition-based memetic algorithm (DBMA) is used. In the proposed approach, (1) five initial rules are presented to generate a high-quality population; (2) crossover and mutation operators are designed to increase the exploration capability; (3) three effective neighborhood structures are designed to increase the exploitation capability; and (4) a new Tchebycheff aggregation method based on a subproblem decomposition-based (SDB) strategy is proposed to select high-quality individuals and design acceptance criterion. The improved performance of this method is demonstrated by comparison with two other effective methods. To verify the effectiveness of DBMA, it is compared with five other famous algorithms on 30 benchmarks. Computational results show that the DBMA can obtain better solutions than other algorithms when solving the IT2GFJSP.