A hybrid multi-objective gray wolf optimization algorithm for a fuzzy blocking flow shop scheduling problem

Abstract

Blocking flow shop scheduling problems have important applications in manufacturing. Because of the imprecise and vague temporal parameters in real-world production, this article formulates a fuzzy blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date in order to minimize the fuzzy makespan and maximize the average agreement index. To solve this combinational optimization problem, a hybrid multi-objective gray wolf optimization algorithm is proposed. The hybrid multi-objective gray wolf optimization utilizes the largest position value rule for solution representation, employs a dynamic maintenance strategy to maintain an archive, and develops a thorough mechanism for leader selection. In the hybrid multi-objective gray wolf optimization, a novel heuristic process is designed to generate initial solutions with a certain quality, and a local search strategy is embedded to improve the exploitation capability. The performance of the hybrid multi-objective gray wolf optimization is tested on the production instances of panel block assembly in shipbuilding. Computational comparisons of the hybrid multi-objective gray wolf optimization with two other well-known multi-objective evolutionary algorithms demonstrate the feasibility and effectiveness of the hybrid multi-objective gray wolf optimization in generating optimal solutions to the bi-criterion fuzzy blocking flow shop scheduling problem.