A new many-objective green dynamic scheduling disruption management approach for machining workshop based on green manufacturing. (15th May 2021)

• Record Type: Journal Article
• Authors: Sang, Yanwei; Tan, Jianping; Liu, Wen
• Is Part Of: Journal of cleaner production. Volume 297(2021)
• Publication Date: 2021-05-15
• Subjects: Many-objective dynamic scheduling problem -- Many-objective optimization -- NSGA-III -- Disruption management -- Green workshop scheduling; 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.2021.126489
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Since disturbance events such as equipment failure often occur in the production process of machining workshop, the scheduling plan needs to be rescheduled. However, the existing rescheduling strategy is difficult to effectively reduce the deviation between the rescheduling plan and the initial scheduling plan. In this article, a new disruption management method is proposed. It includes the disruption management model and the many-objective optimization algorithm. The production scheduling system is a typical man-machine system. The disruption management model not only measures scheduling stability and robustness but also measures the dissatisfaction degree of behavioral agent. It is conducive to comprehensively and scientifically measures the deviation between the rescheduling plan and the initial scheduling plan. When solving the disruption management model, the existing many-objective optimization algorithm cannot effectively balance convergence and diversity. A new algorithm named NSGA–III–RPE is proposed. The NSGA–III–RPE designs improved the r-dominance relationship and a new elite strategy to dynamically transform the Pareto set into the partially ordered set. These improved operations can increase the selection pressure and improve the convergence speed. Different from traditional rescheduling strategy, the new disruption management can comprehensively measure disturbance deviation including the behavioral agents and physical entities in the system, and use NSGA-Ⅲ-RPE algorithm to optimize the disruption management model. Thus, it can quickly obtain an adjusted scheduling plan that has the smallest deviation from the initial scheduling plan. Experimental results on benchmarks also demonstrate the effectiveness and competitiveness of the NSGA–III–RPE algorithm. IGD and HV indicators of NSGA-Ⅲ-RPE are superior to other algorithms. Finally, the new disruption management method is applied to the machining workshop. Compared with the results of total rescheduling method, robustness, stability, and the satisfaction degree of customer increase by 37.25%, 34.14%, 13.87% respectively. It can effectively reduce the deviation between the rescheduling plan and the initial scheduling plan. The new disruption management method is conducive to improve production efficiency, stability and reduce process costs in dynamic manufacturing environment of the machining workshop.