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

ABSTRACT

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.

KEYWORDS:

• Digital twin
• large-scale automated high-rise warehouse
• warehouse product-service system
• storage assignment
• cyber-physical systems

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Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant No. 51705091 and 51675108; the 2030 Innovation Megaprojects of China (Programme on New Generation Artificial Intelligence) under Grant No. 2018AAA0101700; the Science and Technology Planning Project of Guangdong Province of China under Grant No. 2019A050503010, 2019B090916002, and No. 2016A010106006; the Science and Technology Plan Project of Guangzhou under Grant No. 201804020092; the Shenzhen Science and Technology Innovation Committee under Grant No. JCY20170818100156260; the Hong Kong Scholars Program under Grant No. XJ201817.

Disclosure statement

No potential conflict of interest was reported by the authors.