Cloud-based Cyber-Physical Logistics System with Nested MAX-MIN Ant Algorithm for E-commerce logistics. (January 2023)

• Record Type: Journal Article
• Authors: Lee, Carman Ka Man; Ng, Chun Kit; Chung, Sui Ying; Keung, Kin Lok
• Is Part Of: Expert systems with applications. Volume 211(2023)
• Publication Date: 2023-01
• Subjects: Vehicle routing problem -- Ant Colony Optimization -- Order assignment problem -- Meta-heuristic technique -- Cyber-Physical Logistics System; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118643
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An effective nested max–min ant algorithm for VRP model is proposed. VIP priority and drop-off point has been considered. Nested max–min ant system for VRP model considered a set of possible scenarios. The numerical experiments show that the CPLS is implemented successfully. The model can solve the possible scenarios within reasonable computation times. Abstract: E-commerce logistics have required rapid and efficient e-fulfillment services to deliver goods to consumers within a short time window to fulfill the customer order requirement. This paper seeks to address how an E-commerce Logistics Company can utilize the Cyber-Physical Logistics System (CPLS) and Internet of Things (IoT), which may unlock the potential for solving the order assignments and the vehicle routing problems on a new twist. The CPLS allocates orders to suitable vehicles and plans the best route in order to maximize the order fulfillment rate through cloud computing to reduce local computation power. This paper presents a novel Nested MAX-MIN Ant System (MMAS) to tackle a Vehicle Routing Problem (VRP) that can serve as the core element of the CPLS. The objective is to perform to maximize the orders, minimize the total traveling cost and satisfy all the problem constraints. This paper intends to address the issues associated with the VIP priority and drop-off point limitations to make the solution route more realistic and enhance accuracy. The numerical experiences show that the CPLS is implemented successfully in a Hong Kong-based E-commerce Logistics Company with promising performance within reasonable computation times and new better solutions.