A hybrid multi-objective evolutionary algorithm for solving an adaptive flexible job-shop rescheduling problem with real-time order acceptance and condition-based preventive maintenance

Abstract

Production scheduling and maintenance planning are two of the most important tasks in the modern manufacturing workshop. Meanwhile, due to the dynamic order arrival and real-time machine monitoring information updating, the integrated optimization of them becoming more complex and meaningful. Therefore, this study intends to address an adaptive flexible job-shop rescheduling problem with real-time order acceptance (ROA) and condition-based preventive maintenance (CBPM). More precisely, the main innovative works are described as follows: (1) a CBPM policy with both imperfect preventive maintenance (PM) and four inspection strategies is designed to find the optimal maintenance planning for each production machine; (2) a multi-objective optimization model is developed for the concerned problem; and (3) a hybrid multi-objective evolutionary algorithm (HMOEA) with hybrid initialization method, hybrid local search operators and adaptive rescheduling strategies is proposed. In the numerical simulation, the performance and competitiveness of the proposed CBPM policy are first demonstrated by comparing with other maintenance policies. Second, the effectiveness and superiority of parameter setting, order sorting rules, improved operators and overall performance of the proposed algorithm are verified by internal analysis of the algorithm. Third, an adaptive rescheduling strategy pool is constructed by running three rescheduling strategies on all rescheduling scenarios. Finally, a comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the adaptive rescheduling problem, and the results and comparisons show that the proposed HMOEA algorithm and order acceptance strategy have good robustness in most parameters.