Robust Possibilistic Programming Approach to Production Scheduling of Reconfigurable Manufacturing System Based on Learning Effect

The emergence of reconfigurable manufacturing offers innovative solutions for efficiently adapting to changing market demands and system modifications. This paper introduces a robust possibilistic programming framework to address a multi-objective production scheduling problem within sustainable reconfigurable manufacturing systems, incorporating uncertainty. The model captures the workforce learning effect on reconfiguration times, aiming to minimize makespan, production costs, and social sustainability while considering uncertain parameters. Possibilistic chance-constrained programming and robust possibilistic programming approaches are applied to assess both model and solution robustness. Additionally, the framework addresses workplace safety risks linked to workforce assignments and incorporates workforce entry preferences for flexible hours. By considering the learning effect in reconfiguration times, the model reflects the dynamic nature of scheduling, aligning more closely with real-world scenarios. The augmented epsilon-constraint method is also used to efficiently find Pareto-optimal solutions for the multi-objective model.