Abstract:

To enhance the efficiency of emergency supplies distribution after an earthquake, an emergency vehicle routing optimization problem was investigated under limited transportation capacity and the need to simultaneously deliver multiple categories of emergency supplies, while road conditions and demand urgency were jointly considered. Firstly, vehicle travel speeds were corrected based on road damage rates, and a demand urgency evaluation method is established by incorporating key characteristics of earthquake disasters. Then, a post-earthquake emergency supplies distribution optimization model was formulated to minimize the total delivery time and the total urgency-weighted demand cost, involving multiple depots, multiple affected sites, multiple types of emergency supplies, and heterogeneous vehicle fleets. Next, a HEA integrating the fast non-dominated sorting genetic algorithm (NSGA-II) with variable neighborhood search (VNS), is then designed to solve the model. Finally, a case study based on the 2008 Wenchuan (5.12) earthquake was constructed, and simulation experiments were conducted to validate the effectiveness and feasibility of the proposed model and algorithm. The results show that HEA outperforms three benchmark multi-objective optimization algorithms in both solution-set convergence and overall solution quality, and can provide emergency decision-makers with a diverse set of trade-off solutions within a short computation time (average 115.0 s).

Key words: emergency supplies, delivery route, road damage, demand urgency, hybrid evolutionary algorithm (HEA)