Discrete Gray Wolf Optimizer for Solving Distributed Permutation Flowshop Scheduling Problem

Abstract

Distributed manufacturing has become a mainstream production mode in economic globalization. A discrete gray wolf optimizer (DGWO) is proposed to solve the distributed permutation flowshop scheduling problem (DPFSP) to minimize makespan. First, an extended Nawaz-Enscore-Ham2 (ENEH2) and a randomly generated hybrid initialization method are used to enhance the diversity and ergodicity of the population. Second, a discrete population update mechanism is proposed for the characteristics of the solved problem to balance exploration and exploitation. The variable neighborhood descent search strategy is used to further improve the quality of the solution. Finally, the Wilcoxon signed rank and the Friedman test are used for statistical comparison analysis. To verify the performance of the DGWO, simulation experiments are conducted on different scales of instances and compared with various methods to demonstrate the advantages of the DGWO for solving the DPFSP.