A hybrid multi-objective evolutionary algorithm to integrate optimization of the production scheduling and imperfect cutting tool maintenance considering total energy consumption. (20th September 2020)

• Record Type: Journal Article
• Authors: An, Youjun; Chen, Xiaohui; Zhang, Ji; Li, Yinghe
• Is Part Of: Journal of cleaner production. Volume 268(2020)
• Publication Date: 2020-09-20
• Subjects: Production scheduling -- Imperfect cutting tool maintenance -- Total energy consumption -- Hybrid multi-objective evolutionary algorithm; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.121540
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: With the increase in awareness of energy conservation and emission reduction in the manufacturing sector, the energy efficiency optimization of the production process is considered an effective way to promote cleaner production and environmentally sustainable development. Moreover, the transportation process and its energy consumption are non-negligible during the entire workshop production process. In practice, cutting tool degradation during operation is inevitable, which causes the actual processing time of work to be extended, so it is necessary to consider the degradation effects and imperfect maintenance. To reduce the total energy consumption in the manufacturing workshop with degradation effects and imperfect maintenance, this paper constructs a novel integrated optimization model including the flexible job-shop scheduling problem (FJSP), forklift transportation and imperfect cutting tool maintenance. First, an imperfect cutting tool maintenance with degradation effect strategy is presented, which makes the actual processing time of each operation more closely approximate the real-world processing situation. Second, an integrated multi-objective optimization model is formulated to simultaneously minimize the makespan, total tardiness, total production cost and total energy consumption. Third, a hybrid multi-objective evolutionary algorithm with Pareto elite storage strategy (HMOEA/P) is proposed to address the model. More precisely, three improved operations are presented: hybrid dominance principle, local search algorithms and Pareto elite preservation strategy. The effectiveness and feasibility of the parameter setting, improved operations and the proposed algorithm are separately demonstrated by the comparative experiments. Last, the superiority of the integrated multi-objective optimization model is proven by comparing different maintenance strategies and energy consumption models.