Vehicle Routing Optimization for Vaccine Distribution Considering Reducing Energy Consumption

Runfeng Yu, Lifen Yun, Chen Chen, Yuanjie Tang, Hongqiang Fan () and Yi Qin
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Runfeng Yu: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Lifen Yun: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Chen Chen: QI-ANXIN Group, Beijing 100044, China
Yuanjie Tang: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Hongqiang Fan: School of Modern Post, Beijing University of Posts and Telecommunications, Beijing 100876, China
Yi Qin: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Sustainability, 2023, vol. 15, issue 2, 1-24

Abstract: In recent years, the energy consumption of vehicles has gained widespread attention due to the increasing importance of energy and environmental issues. Coupled with the explosive demand for vaccines that has spawned the massive deployment of refrigerated trucks, energy savings and efficiency improvement are the goals pursued by pharmaceutical logistics companies while getting the vaccine distribution task done. In order to evaluate the fuel consumption of refrigerated trucks during vaccine distribution, in this paper, we construct a mathematical model for the vehicle routing problem with time windows (VRPTW) for vaccine distribution with the aim of minimizing the total cost, including fossil fuel cost and penalty cost. Due to the NP -hardness and nonlinearity of the model, a genetic algorithm with a large neighborhood search operator (GA-LNS) and TSP-split encoding method is customized to address the large-scale problem. Numerical experiments show that the algorithm can obtain a near-optimal solution in an acceptable computational time. In addition, the proposed algorithm is implemented to evaluate a case of vaccine distribution in Haidian, Beijing, China. Insights on the effects of seasonal temperature, vehicle speed, driver working hours, and refrigeration efficiency are also presented.

Keywords: vehicle routing problem; low-carbon-based transport operation system; genetic algorithm; large neighborhood search (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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