Robust scheduling for flexible machining job shop subject to machine breakdowns and new job arrivals considering system reusability and task recurrence - Duan, Jianguo; Wang, Jiahui (Expert systems with applications, 2022)
•Robust optimization method with dynamic events is proposed.•Reusability of the system and repeatability of the processing tasks are considered.•A dynamic event response strategy (DERS) is proposed.•A particle swarm arithmetic optimization (PSAO) is used to solve the problem.•The results show that the method can effectively improve the robustness performance. This paper focuses on the production scheduling problem of flexible job shops. In the production process of flexible job shop, there are dynamic events such as machine breakdowns or new job arrivals, which will interfere with the implementation of scheduling scheme and reduce the stability of the system. In response to this problem, this paper proposes a new robust optimization method that considers dynamic events and designs two indicators for evaluating the robustness of the system, namely the reusability of the system and the reproducibility of processing tasks. Two indicators are used to evaluate the comprehensive reusability of the system jointly. In the process of system rescheduling, this paper proposes a dynamic event response strategy (DERS) considering the comprehensive reusability of the system and establishes a multi-objective optimization model considering the total energy consumption, the makespan, and the comprehensive reusability of the system. In order to solve the model efficiently and obtain the optimal Pareto frontier, a multi-objective particle swarm arithmetic optimization (PSAO) is proposed in this paper. Finally, this paper designs experiments based on standard data cases, solves them based on this model and compares them with other algorithms. The final results show that in the flexible job-shop scheduling process, this method can effectively adjust the scheduling plan to respond to dynamic events to achieve stable scheduling in an uncertain environment.