Yang Huawei, Zhang Pan, Zhang Peiwen, Zhang Chenxing, Yan Xuxian
School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan, 030006, China.
Sci Rep. 2024 Dec 30;14(1):31983. doi: 10.1038/s41598-024-83670-3.
To enhance the level of emergency supplies deployment during earthquake disaster, this study focuses on emergency logistics in China. An integrated two-stage optimization framework is adopted to incorporate demand and time satisfaction indicators into the supply allocation and route optimization models, respectively. Firstly, historical data and seismic monitoring information are used to estimate the number of people affected and to forecast the need for emergency supplies; Secondly, the concept of psychological risk perception and the degree of urgency of requirements are introduced. Based on the modified prospect theory framework, this article replaces the sufficiency and shortage of demand with gains and losses to optimize the resource allocation policy. Thirdly, Particle Swarm Optimization (PSO) is used to improve the Sparrow Search Algorithm (SSA) for further model solving. The results of the study show that the two-stage optimization framework can significantly improve the rescue efficiency and rationality of resource allocation, and achieve the goal of prioritising the distribution of emergency supplies to regions with high urgency; In addition, the results of the sensitivity analysis indicate that it is crucial to determine the optimal proportion of the total amount of supplies, and the validation shows that the overall operational efficiency of PSO- SSA is higher, which provides a more reliable approach to dealing with similar emergency problems.
为提高地震灾害期间应急物资调配水平,本研究聚焦于中国的应急物流。采用一个集成的两阶段优化框架,将需求和时间满意度指标分别纳入供应分配和路线优化模型。首先,利用历史数据和地震监测信息来估计受影响人数并预测应急物资需求;其次,引入心理风险感知概念和需求紧急程度。基于改进的前景理论框架,本文用收益和损失取代需求的充足与短缺来优化资源分配策略。第三,使用粒子群优化算法(PSO)改进麻雀搜索算法(SSA)以进一步求解模型。研究结果表明,两阶段优化框架能显著提高救援效率和资源分配的合理性,实现将应急物资优先分配到紧急程度高的地区的目标;此外,敏感性分析结果表明确定物资总量的最优比例至关重要,验证表明PSO-SSA的整体运行效率更高,为处理类似应急问题提供了更可靠的方法。