Multimodal Transportation Route Optimization of Emergency Supplies Under Uncertain Conditions

Zhongyan Xu, Changjiang Zheng (), Shukang Zheng, Genghua Ma and Zhichao Chen
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Zhongyan Xu: College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Changjiang Zheng: College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Shukang Zheng: College of Environment, Hohai University, Nanjing 210098, China
Genghua Ma: Coastal and Offshore Engineering, College of Harbour, Hohai University, Nanjing 210098, China
Zhichao Chen: College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China

Sustainability, 2024, vol. 16, issue 24, 1-26

Abstract: In recent years, the frequency of sudden disaster events has increased significantly, leading to substantial and often irreparable losses for society and individuals. Although the comprehensive development of China’s transportation network has laid a solid foundation for the transportation of emergency supplies, the construction of an optimized transportation system for emergency logistics still faces considerable challenges, particularly given the uncertainties of the transportation environment. To ensure the efficient delivery of emergency supplies to disaster sites via optimal routes and the utilization of multimodal transport for logistics distribution, this paper addresses the following key objectives. First, recognizing the uncertainties in parameters such as transportation time and cost due to dynamic changes in the transportation environment as well as the limitations of transshipment capacity, we developed a multimodal transport path optimization model for emergency supplies. The model aims to minimize both transportation time and costs. Second, in addressing uncertain parameters, we employed the theory of interval number ranking, defining a risk coefficient to convert uncertain parameters into deterministic ones. This enabled a deterministic transformation of the model. Additionally, a weighted scoring method was applied to assign weights to each objective, effectively transforming the multi-objective optimization problem into a single-objective problem. Finally, we applied the genetic algorithm, particle swarm optimization algorithm, and a hybrid genetic–particle swarm algorithm to solve the model. The effectiveness of the model and the superiority of the hybrid algorithm were verified through case studies. Furthermore, a sensitivity analysis of the parameters and an investigation into the influence of the risk coefficient were conducted, providing valuable insights for relevant decision-making departments in formulating emergency material transportation plans.

Keywords: uncertain conditions; emergency supplies; multimodal transportation; path optimization; hybrid genetic–particle swarm algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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