In the early stage of large-scale disasters, the first batch of emergency supplies are often in short supply, and decision-makers responsible for material distributions need to send emergency materials to the recipients in the shortest possible time, while also taking into account the minimum transportation costs. In these scenarios, the traditional particle swarm algorithm has been frequently used, however it faces the challenge of “precocious puberty" and is unable to resolve the scheduling problem. To solve this issue, this paper proposes an optimization model for material dispatch in emergency events using a non-dominant sorting algorithm for vehicular communication. The model first satisfies the shortest delivery time and material demand, establishes the shortest route for vehicle travel, and then proposes a multi-objective uncontrolled solving ant colony algorithm to break through the bottleneck of the juvenile algorithm by solving the problems of convergence of NSGA-II algorithm and uneven distribution of Pareto front surface. Moreover, the objective function and constraints for vehicles at each emergency supply point are defined, which must not exceed the total number of available vehicles. The case study shows that the Pareto non-inferior solution searched by NSGA-II is ideal under the premise that multiple goals are optimal, and the Pareto non-inferior solution scheme available for researchers to choose is improved. The model and algorithm objectively optimize the overall layout of emergency material distribution.

在大规模灾害发生初期，第一批应急物资往往供不应求，负责物资分发的决策者需要在最短的时间内将应急物资送到受援者手中，同时还要考虑到最低的运输成本。在这些场景中，传统的粒子群算法被频繁使用，但面临“性早熟”的挑战，无法解决调度问题。针对这一问题，本文提出了突发事件物资调度优化模型。使用非优势排序算法进行车辆通信，该模型首先满足最短的交货时间和物资需求，建立车辆行驶的最短路线，然后通过解决NSGA-II算法收敛性和Pareto前表面分布不均匀的问题，提出了一种多目标无控求解蚁群算法，以突破幼稚算法的瓶颈。此外，定义了每个应急供应点车辆的目标函数和约束条件，不得超过可用车辆的总数。案例研究表明，NSGA-II搜索到的Pareto非劣解在多目标最优的前提下是理想的，改进了可供研究人员选择的Pareto非劣解方案。该模型和算法客观地优化了应急物资配送的整体布局。