Optimal decision-making of post-disaster emergency material scheduling based on helicopter–truck–drone collaboration

无人机 卡车 解算器 调度(生产过程) 启发式 应急管理 计算机科学 整数规划 运筹学 作业车间调度 自然灾害 工程类 运营管理 算法 人工智能 地铁列车时刻表 生物 遗传学 物理 气象学 法学 政治学 程序设计语言 航空航天工程 操作系统
作者
Yong Shi,Junhao Yang,Qian Han,Hao Song,Haixiang Guo
出处
期刊:Omega [Elsevier BV]
卷期号:127: 103104-103104 被引量:60
标识
DOI:10.1016/j.omega.2024.103104
摘要

In the last decades, natural disasters, such as earthquakes and landslides, have occurred frequently, seriously threatening the safety of people's lives and property. How emergency material is scheduled and delivered efficiently to the affected sites after a disaster has become a critical issue in emergency management. Current studies on emergency material scheduling mainly focus on truck or helicopter transport. Inspired by the success of employing drones in commercial logistics, this work investigates the emergency material scheduling issue based on the cooperative transport of drones, helicopters, and trucks. Specifically, this paper considers the limited transport capacity, road conditions in the early stage of the disaster rescue, and affected sites restricted by road conditions that can only be served by helicopters or drones. The studied problem is formulated as a mixed integer programming model, and a two-stage heuristic algorithm is developed to solve the model. For the proposed model, instances of different sizes are generated, and extensive experiments are performed to test the efficiency of the proposed algorithm. The comparison between the solutions obtained by the two-stage algorithm and Gurobi Solver for the small instances validates the effectiveness of the proposed heuristic algorithm. Experimental results for the larger instances show that the proposed two-stage algorithm can effectively solve the problem of emergency material scheduling. Sensitivity analysis of ten typical instances is performed to provide managerial insights. Finally, a case study of the Sichuan earthquake and the visualization of transport routes are presented. The model and solving approach proposed in this work can provide essential decision references for emergency management decision-making.
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