已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Planning for Medical Emergency Transportation Vehicles during Natural Disasters

自然灾害 计算机科学 运输工程 医疗急救 自然(考古学) 环境规划 运筹学 环境科学 医学 历史 工程类 地理 气象学 考古
作者
Hesam Adrang,Ali Bozorgi-Amiri,Kaveh Khalili‐Damghani,Reza Tavakkoli‐Moghaddam
出处
期刊:DOAJ: Directory of Open Access Journals - DOAJ 被引量:3
标识
DOI:10.22094/joie.2020.688.1455
摘要

One of the main critical steps that should be taken during natural disasters is the assignment and distribution of resources among affected people. In such situations, this can save many lives. Determining the demands for critical items (i.e., the number of injured people) is very important. Accordingly, a number of casualties and injured people have to be known during a disaster. Obtaining an acceptable estimation of the number of casualties adds to the complexity of the problem. In this paper, a location-routing problem is discussed for urgent therapeutic services during disasters. The problem is formulated as a bi-objective Mixed-Integer Linear Programming (MILP) model. The objectives are to concurrently minimize the time of offering relief items to the affected people and minimize the total costs. The costs include those related to locations and transportation means (e.g., ambulances and helicopters) that are used to carry medical personnel and patients. To address the bi-objectiveness and verify the efficiency and applicability of the proposed model, the e-constraint method is employed to solve several randomly-generated problems with CLEPX solver in GAMS. The obtained results include the objective functions, the number of the required facility, and the trade-offs between objectives. Then, the parameter of demands (i.e., number of casualties), which has the most important role, is examined using a sensitivity analysis and the managerial insights are discussed.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
刚刚
完美世界的应助被blue采纳,获得10
刚刚
刚刚
专一的砖头完成签到,获得积分10
3秒前
aaa完成签到 ,获得积分10
3秒前
likangmiao发布了新的文献求助10
4秒前
4秒前
医学科研小白完成签到,获得积分10
5秒前
5秒前
你猜我猜不猜你在猜完成签到,获得积分10
7秒前
王大壮完成签到,获得积分0
9秒前
9秒前
10秒前
Wendy完成签到 ,获得积分10
10秒前
10秒前
今后的应助被呀呀呀采纳,获得10
10秒前
78888完成签到 ,获得积分10
11秒前
寒冷紫槐发布了新的文献求助10
11秒前
bobo完成签到,获得积分10
11秒前
12秒前
hh发布了新的文献求助10
15秒前
lx840518完成签到 ,获得积分10
18秒前
uo完成签到 ,获得积分10
18秒前
拾光给拾光的求助进行了留言
19秒前
黄斑反光可见的应助被zsj采纳,获得10
20秒前
zhenggc完成签到,获得积分10
20秒前
20秒前
bkagyin的应助被XD采纳,获得10
22秒前
25秒前
所所的应助被明月采纳,获得10
29秒前
曼曼完成签到,获得积分10
31秒前
Verity的应助被初景采纳,获得10
31秒前
健壮灯泡完成签到,获得积分10
32秒前
呀呀呀发布了新的文献求助10
32秒前
友好的尔丝完成签到 ,获得积分10
34秒前
辛勤曼容完成签到 ,获得积分10
35秒前
36秒前
科研通AI6.4的应助被zsj采纳,获得10
37秒前
lll发布了新的文献求助10
37秒前
无辜的若枫完成签到 ,获得积分10
39秒前
高分求助中
(应助此贴封号)通过应助OA文献获取积分 10000
Rosenblum, Global Change Biology 800
Computational Chemical Reaction Engineering: Modeling, Simulation, and Design with MATLAB 600
Organizational Behavior 510
Management and the Arts 510
A Will for the Machine: Computerization, Automation, and the Arts in South Africa 400
Decentring Leadership 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 内科学 物理 有机化学 化学工程 生物化学 复合材料 光电子学 细胞生物学 心理学 量子力学 催化作用 物理化学 电极
热门帖子
关注 科研通微信公众号,转发送积分 7809301
求助须知:如何正确求助?哪些是违规求助? 9341585
关于积分的说明 20507429
捐赠科研通 7401805
什么是DOI,文献DOI怎么找? 3329074
关于科研通互助平台的介绍 2475843
邀请新用户注册赠送积分活动 2347644