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:14
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
无限尔云发布了新的文献求助10
2秒前
3秒前
albertchan完成签到,获得积分10
4秒前
5秒前
WHHEY完成签到,获得积分20
5秒前
量子星尘发布了新的文献求助10
6秒前
热心市民小红花应助HH采纳,获得30
6秒前
6秒前
超帅连虎发布了新的文献求助10
8秒前
早点毕业完成签到 ,获得积分10
9秒前
SYLH应助WHHEY采纳,获得30
10秒前
李健的小迷弟应助Little2采纳,获得10
10秒前
wanci应助无限尔云采纳,获得10
12秒前
迅速的奇异果完成签到,获得积分10
12秒前
998发布了新的文献求助30
12秒前
Pooh完成签到 ,获得积分10
14秒前
14秒前
20秒前
Little2发布了新的文献求助10
20秒前
柒月给柒月的求助进行了留言
21秒前
lzd完成签到,获得积分10
23秒前
23秒前
Nikola完成签到 ,获得积分10
24秒前
pups发布了新的文献求助10
31秒前
急雪回风完成签到,获得积分10
33秒前
37秒前
落忆完成签到 ,获得积分10
39秒前
不会失忆完成签到,获得积分10
40秒前
高挑的涛发布了新的文献求助10
41秒前
NexusExplorer应助科研通管家采纳,获得10
41秒前
天天快乐应助科研通管家采纳,获得10
41秒前
夜无霜666完成签到,获得积分10
43秒前
支妙完成签到,获得积分10
43秒前
十八发布了新的文献求助10
44秒前
QWDSA发布了新的文献求助10
46秒前
bioglia完成签到,获得积分10
46秒前
文武发布了新的文献求助30
47秒前
47秒前
bxyyy完成签到 ,获得积分10
47秒前
李健的粉丝团团长应助yyt采纳,获得10
48秒前
高分求助中
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Cognitive Neuroscience: The Biology of the Mind (Sixth Edition) 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 588
Christian Women in Chinese Society: The Anglican Story 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3961020
求助须知:如何正确求助?哪些是违规求助? 3507251
关于积分的说明 11134825
捐赠科研通 3239661
什么是DOI,文献DOI怎么找? 1790305
邀请新用户注册赠送积分活动 872341
科研通“疑难数据库(出版商)”最低求助积分说明 803150