Metaverse-Based Order Picking Optimization for Supply Chain of Things

In recent years, with the rise of e-commerce and digital services, customers' expectations have affected supply chain management operations. The logistics industry reacts proactively by integrating new technologies into its systems to meet changing customer expectations. Among these technologies, a relatively new concept called Supply Chain of Things (SCoT) is an enabling technology for supply chain operations to increase productivity and automation. However, a large number of customer orders and increasing concerns about operating costs challenge efficient management and quality of service in SCoT. In this regard, this paper addresses the order-picking problem of minimizing delivery time and operating costs, including travel costs and total costs to the employer. Metaverse can be considered a feasible solution to address this problem due to its advantages of seamless and intelligent interaction between the physical world and the digital world. In this paper, we present a metaverse-based system architecture and design the network digital twin to enable intelligent real-time management of the SCoT environment. We formulate the order-picking problem and propose a genetic algorithm-based solution to satisfy customer demands on time with minimal operating costs by creating a digital twin of the supply chain system in the Metaverse. The simulation results show that the proposed solution achieves significant gains compared with baseline strategies in terms of the total cost to the employer while keeping order delivery time under the acceptable customer level.