A bi-evolutionary cooperative multi-objective algorithm for blocking group flow shop with outsourcing option

• Published in: Expert systems with applications Vol. 258; p. 125101
• Main Authors: Wang, Xinrui, Li, Junqing, Zhang, Yuanyuan, Gao, Kaizhou, Zheng, Zhixin, Li, Jiake, Xu, Ying
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2024
• Subjects: Blocking group flow shop scheduling; Co-evolutionary algorithm; Multi-objective; Outsourcing option; Blocking group flow shop scheduling; Multi-objective; Outsourcing option; Co-evolutionary algorithm
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125101

In this study, a multi-objective blocking group flow shop scheduling problem with outsourcing option (BGFSP_OO) is addressed, where three objectives, including makespan, total energy consumption (TEC) and outsourcing cost, are considered simultaneously. To solve the BGFSP_OO, a bi-evolutionary cooperative multi-objective algorithm (BECMOA) is proposed. First, a machine switching strategy based on machine idle and blocking time is used in the decoding part to optimize the objective value TEC. Then, an effective heuristic for classifying outsourcing groups is proposed. To balance convergence and diversity abilities, a bi-evolutionary mechanism is proposed. The particle swarm optimization algorithm based on the gravity factor (IPSO) is employed, which exploiting individual performance, can enhance the convergence ability. Cross evolutionary search (CES) strategy is developed which can improve search ability, aiming to ensure diversity of solutions and access to more non-dominated solutions. Finally, the experimental results show that BECMOA is effective in solving BGFSP_OO compared to the state-of-the-art methods.