Digital twin-driven joint optimisation of packing and storage assignment in large-scale automated high-rise warehouse product-service system

Current mass individualisation and service-oriented paradigm calls for high flexibility and agility in the warehouse system to adapt changes in products. This paper proposes a novel digital twin-driven joint optimisation approach for warehousing in large-scale automated high-rise warehouse product-service system. A Digital Twin System is developed to aggregate real-time data from physical warehouse product-service system and then to map it to the cyber model. A joint optimisation model on how to timely optimise stacked packing and storage assignment of warehouse product-service system is integrated to the Digital Twin System. Through perceiving online data from the physical warehouse product-service system, periodical optimal decisions can be obtained via the joint optimisation model and then fed back to the semi-physical simulation engine in the Digital Twin System for verifying the implementation result. A demonstrative prototype is developed and verified with a case study of a tobacco warehouse product-service system. The proposed approach can maximise the utilisation and efficiency of the large-scale automated high-rise warehouse product-service system.