Optimization of vehicle routing problems combining the demand urgency and road damage for multiple disasters

Abstract

In the context of the COVID-19 epidemic, a “double-hazard scenario” consisting of a natural disaster and a public health event simultaneously occurring is more likely to arise. However, compared with single-hazard, multiple disasters confront the challenges of complexity, diversity, and demand urgency. To improve the efficiency of emergency material distribution under multiple disasters, this study first divided multiple disasters into three categories: independent scenario, sequential scenario, and coupling scenario. A set of evaluation index systems for multiple disasters was established to quantify the urgency of demand. The routing optimization model of emergency vehicles for multiple disasters was proposed by combining demand urgency and road damage, and the non-dominated sorting genetic algorithm II (NSGA-II) was used to simulate and validate the model. A coupling scenario considering two typical disasters of hurricanes and epidemics was selected as a validation example, and sensitivity analysis was also performed for different algorithms, scenarios, and constraints. The results demonstrated that the proposed model could effectively address the vehicle routing problem of emergency materials in the context of multiple disasters. Compared to the NSGA, the NSGA-II was used to reduce the total delivery time, cost, and penalty cost by 15.98%, 13.60%, and 16.14%, respectively. Compared with the independent scenario, the coupling scenario increased the total delivery time and cost by 186.28% and 132.48% during the epidemic. However, it reduced the total delivery time by 4.00% and increased the delivery cost by 23.55% compared with the hurricane. Compared with the model without consideration, the model considering demand urgency and road damage reduced the total delivery time and cost by 17.88% and 8.73%, respectively. The model constructed in this study addressed the vehicle routing problem considering the demand urgency and road damage in the optimization process, particularly in the context of multiple disasters.