Dynamic coordinated scheduling for supply chain under uncertain production time to empower smart production for Industry 3.5

To empower smart production for supply chain management, scheduling coordination and integration between suppliers, manufacturers, distributors, and customers is becoming increasingly important. Indeed, fluctuations in production time are not fully predictable, especially in the dynamic contexts of manufacturing systems. Existing approaches, based on constant processing time, cannot appropriately address coordinated scheduling in a supply chain, yet little research has addressed the present problem. Focusing on dynamic features in real settings, this study aims to propose a strategy that integrates event- and period-driven methods to enhance the stability and robustness of manufacturing systems in a coordinated supply chain. In particular, this study integrated hybrid particle swarm optimization and genetic algorithm to minimize the uncertain makespan of coordinated scheduling to empower smart production for Industry 3.5. Experiments are designed to compare scenarios associated with different problem scales for validation. The results have shown practical viability of the proposed approach.