Abstract While the static integrated process planning and scheduling (IPPS) problem is theoretically well-established, its practical application is limited in unpredictable manufacturing environments demanding dynamic adaptability. This paper proposes a dynamic IPPS problem considering stochastic rework (IPPS-SR), whose solution optimizes product quality and scheduling performance when imperfect items require reprocessing. We first formulate a mathematical optimization model for IPPS-SR that minimizes makespan and schedule instability, and then present an event-driven hybrid rescheduling approach featuring two key innovations: (1) a hybrid strategy that integrates right-shift scheduling with a multi-objective reinforcement learning-guided adaptive large neighborhood search (MORL-ALNS) algorithm, achieving an effective trade-off between computational efficiency and solution quality; and (2) a set of problem-specific operators, including five destroy and four repair operators, that enhance the search efficacy of the MORL-ALNS framework. Experimental results on 24 adapted benchmark instances indicate that the proposed hybrid approach effectively generates high-quality rescheduling schemes for the IPPS-SR problem. Specifically, the RL-guided mechanism increases the number of non-dominated solutions by over 80% on average compared to the baseline ALNS. Comprehensive experiments with other widely used multi-objective algorithms further demonstrate that MORL-ALNS achieves superior Hypervolume (HV) values in 22 out of 24 instances and lower Inverted Generational Distance (IGD) values in 23 out of 24 instances.
Shi et al. (Mon,) studied this question.