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

Focusing on the dynamic features in real settings, this study proposes a strategy that integrates event and periodic driven methods to improve the stability and robustness of manufacturing performance in a coordinated supply chain. A novel genetic algorithm is developed to minimize the uncertain makespan for coordinated scheduling in supply chain. To estimate the validity of the proposed, an experiment was designed to compare several scenarios in different problem scales. The results have shown practical viability of the proposed approach. [Display omitted] •Dynamic coordinated scheduling for supply chain is addressed for smart production.•Uncertain production time is considered.•A strategy integrating event and periodic driven methods is developed.•Novel genetic algorithm is developed to minimize uncertain makespan for coordinated scheduling.•Experiments are designed to demonstrate its practical viability. 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.