Digital twin-based adaptive opti-state control approach for production-logistics synchronisation system
针对生产物流同步系统受动态扰动影响效率的问题，提出基于数字孪生的自适应最优状态控制方法，通过实时数据驱动决策，提升资源利用率、降低成本，并在涂料制造企业成功应用。
The continuous and stochastic occurrence of dynamic disruptions is seriously impairing the collaborative efficiency of Production-Logistics Synchronisation Systems (PLSS). These disruptions disrupt well-planned production, warehousing, and scheduling processes, lower the utilisation of production and logistics resources, and eventually lead to either system breakdowns or soaring costs. Therefore, the key to building a synchronous, resilient, and sustainable PLSS lies in the ability to rapidly sensing disruptions, coordinate unit operations, and adaptively adjusting system plans and objectives to maintain long-term opti-state performance. This paper first designs a multi-granularity state network to guide the development of a Digital Twin-based Adaptive Opti-state Control System (DT-AOsCS). Subsequently, real-time data-driven Adaptive Opti-state Control (AOsC) mechanism and decision-making model are established to equip the system with rapid responses and adaptive decision-making capabilities. Experimental analysis shows that the proposed AOsC approach improves equipment and warehouse utilisation, enhances production-logistics synchronous efficiency, and effectively reduces total operational costs, minimising the impact of disruptions. Finally, the digital solution was successfully implemented at a paint manufacturing company, helping it mitigate dynamic disruptions, save significant costs, and ensure on-time order delivery.