Two-stage knowledge-driven evolutionary algorithm for distributed green flexible job shop scheduling with type-2 fuzzy processing time

This study is investigated on multi-objective distributed green flexible job shop scheduling problem with type-2 fuzzy processing time. Minimizing makespan and total energy consumption simultaneously is considered. A mixed integer linearity programming model is developed to describe the considered problem. To solve such a hard problem, a two-stage knowledge-driven evolutionary algorithm is proposed (TS-KEA) which divided evolutionary process into two stage. On the first stage, an initial strategy mixed with five problem-specific heuristics is applied to provide a high-quality initial population. Next, a Pareto-based multi-objective evolutionary algorithm is designed for quickly converging to the high quality solutions. Then, a full-active scheduling strategy is designed to reduce total energy consumption. On the second stage, five problem-specific neighborhood structures are proposed to search the non-dominated solutions around the elite solutions. Finally, to evaluate the performance of the proposed algorithm, a number of experiments are adopted on a benchmark with 20 instances. The experiment results show that the proposed TS-KEA can efficiently solve this problem.