Digital twin driven opti-state control approach for smart warehousing in the synchronous operating environment

Abstract

Warehouse operations are increasingly subject to internal variability and external disruptions, resulting in unstable task execution, elevated logistics costs, and inefficiencies in inventory management. A major challenge lies in enabling the warehouse system to maintain optimal performance under such dynamic disturbances. However, existing systems often suffer from low levels of data interoperability, asynchronous operations among resources, and the inability to determine and maintain real-time opti-state. To tackle these limitations, this paper proposes a Digital Twin-driven Warehouse Opti-state Control System (DT-WOsCS), which establishes a synchronous operating environment that enables full-dimensional perception and coordination among Smart Warehousing Objects (SWOs). The framework integrates a multi-scale state sensing with an opti-state control strategy to monitor disturbances and adaptively reconfigure operational plans in real time. A Storage Location Assignment Problem with Mixed-Stacking Areas (SLAP-MSA) is developed under this paradigm, capturing both spatial dynamics and operational constraints. To solve the resulting complex scheduling problem, a customized genetic algorithm is introduced, featuring dual-priority chromosome encoding and adaptive perturbation to support efficient opti-state search. A case study in a paint manufacturing warehouse is conducted to evaluate the proposed method. Experimental results show that DT-WOsCS significantly improves warehouse space utilization, reduces operational and transfer costs, and enhances system resilience and stability in the presence of dynamic disturbances.