Promoting intelligent IoT-driven logistics through integrating dynamic demand and sustainable logistics operations

Jianxin Wang, Ming K. Lim and Weihua Liu

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 185, issue C

Abstract: Developing a more convenient and sustainable logistics delivery operation model has become a consensus need for both academics and practitioners. However, the existing literature has focused on the impact of dynamic features of IoT customers on the distance cost of vehicles (DCV) and quantity cost of vehicles (QCV) in the field of the vehicle routing problem with time windows (VRPTW). Moreover, only a few application scenarios have been considered. This paper focuses on the perspective of intelligent logistics development to research the impact of IoT customers’ dynamic demand on the VRPTW. A mathematical model is established to respond to the dynamic demands of IoT customers with the goal of minimizing the distance cost. A strategy for solving the dynamic VRPTW (DVRPTW) based on time slices is developed, and an improved tabu search (I-TS) optimization algorithm is proposed to solve a given delivery business case. In simulation experiments, the proposed I-TS solution and another known best solution (namely, Solomon) are compared, and the results show the superior performance of the I-TS algorithm in reducing the DCV and QCV. Furthermore, the case study explores the relationship between the degree of dynamism (DoD), DCV and QCV under two scenarios (responding and not responding to dynamic demands of IoT customers). It is concluded that fluctuations of the DoD in a certain range affect only the DCV and not the QCV. Large-capacity vehicles can improve the robustness of the route scheme to dynamic demands. In addition, decomposing dynamic issues in space through time slicing effectively reduces the complexity of the DVRPTW solution. This research also aims to assist practitioners in better formulating dynamic delivery routes as well as policy makers in developing intelligent delivery operation models. Finally, limitations and future research directions are discussed.

Keywords: Intelligent logistics; Dynamic demand; IoT customers; Logistics operations; Improved tabu search (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tre.2024.103539

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