A cooperative grey wolf optimizer for the joint flowshop scheduling problem with sequence-dependent set-up time

With the complexity involved in manufacturing products, many companies use multiple processes to complete product processing. Most studies have been concerned with single production but have neglected the widespread joint flowshop scheduling problem (JFSP). In this article, a cooperative grey wolf optimizer (CGWO) is developed to solve the JFSP. First, according to the features of the JFSP, a corresponding mathematical model is constructed, and three collaborative strategies and random generation are proposed to initialize the population. In the process of searching for prey, a discretized search for prey update mechanism is proposed, which is conducive to balancing exploration and exploitation. An energy-saving strategy is proposed to decrease energy consumption. Moreover, four local search mechanisms are developed for different optimization objectives to enhance the performance of the method in attacking the prey. The results show that the CGWO is effective in solving the JFSP.