Multi-objective flexible flow-shop rescheduling with rigid–flexible hybrid constraints and preventive maintenance

The synergy between production rescheduling and machine maintenance is critical, particularly in cases where unforeseen equipment failures, not fully prevented by maintenance, might threaten the viability of the original plan. In this context, this paper explores a novel integrated optimization problem of production rescheduling and preventive maintenance in a capacity-limited flexible flow-shop (CLFFS), in which random equipment failures, hybrid rigid–flexible constraints of buffer capacity and due window are considered. Specifically, (1) an integrated optimization model is established to minimize the makespan, average flow time, earliness$/$tardiness penalty, machine workload extreme deviation and system instability; (2) an adaptive hybrid rescheduling strategy (AHRS) that amalgamates three classical rescheduling approaches is designed to effectively respond to random equipment failures; and (3) an improved bi-population cooperative evolutionary algorithm with an adaptive environment selection mechanism (AES-IBCEA) is developed to deal with the integrated problem. In the numerical experiments, Taguchi method is first employed to set the parameters of the proposed algorithm. Second, the effectiveness and superiority of designed operators and proposed AES-IBCEA are validated through algorithm comparison. Next, the competitiveness of the proposed AHRS is demonstrated by contrasting it with other rescheduling strategies, and the average improvement rate is up to 22.12%. Finally, a sensitivity analysis on the fault impact threshold (${\delta }_0$) and the individual selection threshold ($\beta$) is performed, and the results reveal that $\beta$ has a significant impact on the algorithm’s performance.