Problem-specific knowledge MOEA/D for energy-efficient scheduling of distributed permutation flow shop in heterogeneous factories

With the development of the global economy and the enhancement of environmental awareness, energy-efficient permutation flow shop scheduling gets more attention. Nevertheless, research on distributed scheduling with heterogeneous factories is scarce. In this paper, a knowledge-driven MOEA/D (KMOEA/D) is proposed to address the energy-efficient scheduling of distributed permutation flow shop problem in heterogeneous factories (DPFSP-HF) with the criteria of minimizing the makespan (Cmax) and total energy consumption (TEC). First, an efficient energy-saving strategy is proposed to reduce the TEC criteria. Second, a constructive heuristic is designed to generate a high-quality solution set. Third, an ingenious genetic operator is utilized to maintain population diversity. Fourth, the knowledge-driven local search operator combined the problem-specific knowledge is constructed according to the properties of DPFSP-HF. Additionally, the Taguchi approach is used to calibrate the parameter configuration of KMOEA/D. We evaluate the effectiveness of each improvement of KMOEA/D and compare it to other well-known multi-objective optimization algorithms on different instances. The results indicate the effectiveness of each improvement of KMOEA/D, and verify that KMOEA/D is an efficient approach to address DPFSP-HF.