大数据背景下台风灾害应急物流车辆调度优化仿真

台风灾害事件发生后的救援阶段,应急救援物资有限且物资需求点对物资需求具有不确定性。现有调度数学模型只考虑了救援车辆调度距离以及时间等因素,没有考虑到提供给受灾点的救援物资可能存在不足或是过量及其对应应急救援效果的影响,导致调度成本过高。针对以上问题,提出基于离散蜂群的台风灾害应急物流车辆调度优化模型。考虑到提供给受灾点的应急物资可能不足或是过量的特点,在设定受灾点所需物资量遵从正态分布的前提下,以最小化物资分配不足和供应过量所带来的损失、车辆调度成本为优化目标,考虑受灾点对服务时间的要求和车辆承载能力等约束,构建了紧迫性需求条件下的调度问题的优化模型,并采用离散蜂群算法对调度问题优化模型进行求解。实验结果表明,所提模型与其他调度数学模型相比,有效降低了应急物流车辆调度成本,可为台风灾害应急管理者提供科学的决策依据。

Typhoon Disaster Emergency Logistics Vehicle Dispatching Optimization Simulation under Big Data Background

In the rescue phase after the typhoon disaster incident, emergency relief supplies are limited and the demand for materials at the material demand point is uncertain. The existing dispatching mathematical model only considers factors such as the distance and time of the dispatch of the rescue vehicles, and does not consider that the relief supplies provided to the affected points may have insufficient or excessive amounts and the impact of the corresponding emergency rescue results, resulting in an excessively high dispatching cost. To solve the above problems, a typhoon disaster emergency logistics vehicle scheduling optimization model based on discrete bee colony is proposed. Taking into account the characteristics of emergency supplies provided to the disaster site may be insufficient or excessive, in the premise of setting the amount of material required for the disaster site to follow a normal distribution, the losses and vehicles caused by underestimation of material resources and oversupply Scheduling cost is the optimization goal, taking into account the constraints of service time requirements and vehicle carrying capacity of the affected site, constructing an optimization model of the scheduling problem under the urgency demand conditions, and using a discrete bee colony algorithm to solve the optimization model of the scheduling problem. The experimental results show that compared with other scheduling mathematical models, the proposed model can effectively reduce the emergency logistics vehicle dispatching cost and can provide scientific decision-making basis for the typhoon disaster emergency managers.