An improved grey wolf optimizer for welding shop inverse scheduling. (January 2022) - Wang, Cuiyu; Zhao, Li; Li, Xinyu; Li, Yang
Highlights: Study a new welding shop inverse scheduling problem (WSISP). Propose the model of welding shop inverse scheduling problem. Propose an improved grey wolf optimizer (IGWO) algorithm with variable neighborhood search to solve the WSISP. The experimental results illustrate that the proposed algorithm is effective for WSISP. Abstract: The welding is widely used in many manufacturing industries. In real-life welding production shop, because of a variety of dynamic events, the original schedule maybe not optimal or even unfeasible. However, if the scheduling scheme is rescheduled blindly, the stability of production and the overall production efficiency will be greatly impacted. To deal with this problem, this paper proposes the model and algorithm of the welding shop inverse scheduling problem (WSISP) for the dynamic events according to welding shop characteristics. Minimizing the parameter adjustment is regarded as the objective. And an improved grey wolf optimizer (IGWO) algorithm is proposed to solve this problem. First, for typical discrete optimization problems, the encoding based on a matrix and the initialization method by the critical path are designed. These methods can avoid unnecessary search and improve search efficiency. Second, a variable neighborhood search method is used to enrich the local search ability of the IGWO algorithm. Afterwards, with the objective of minimizing the parameter adjustment, three kinds of instances are selected to verify the performance of the proposed algorithm, and the comparisons with the state-of-art methods are also conducted. The experimental results show the superiority of the proposed method for solving WSISIP. Finally, a case study considering a real-life welding shop is conducted to validate the proposed method.