Facility location optimization model for emergency humanitarian logistics

被引:283
作者
Boonmee, Chawis [1 ]
Arimura, Mikiharu [1 ]
Asada, Takumi [1 ]
机构
[1] Muroran Inst Technol, Div Sustainable & Environm Engn, Muroran, Hokkaido, Japan
关键词
Facility location problem; Emergency humanitarian logistics; Disaster; Optimization model; ROBUST OPTIMIZATION; OR/MS RESEARCH; OPERATIONS; EARTHQUAKE; DISASTERS; TRANSPORTATION; SUPPLIES;
D O I
10.1016/j.ijdrr.2017.01.017
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Since the 1950s, the number of natural and man-made disasters has increased exponentially and the facility location problem has become the preferred approach for dealing with emergency humanitarian logistical problems. To deal with this challenge, an exact algorithm and a heuristic algorithm have been combined as the main approach to solving this problem. Owing to the importance that an exact algorithm holds with regard to enhancing emergency humanitarian logistical facility location problems, this paper aims to conduct a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types and to examine the pre- and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers. The survey will examine the four main problems highlighted in the literature review: deterministic facility location problems, dynamic facility location problems, stochastic facility location problems, and robust facility location problems. For each problem, facility location type, data modeling type, disaster type, decisions, objectives, constraints, and solution methods will be evaluated and real-world applications and case studies will then be presented. Finally, research gaps will be identified and be addressed in further research studies to develop more effective disaster relief operations.
引用
收藏
页码:485 / 498
页数:14
相关论文
共 84 条
[51]   Locating temporary shelter areas after an earthquake: A case for Turkey [J].
Kilci, Firat ;
Kara, Bahar Yetis ;
Bozkaya, Burcin .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2015, 243 (01) :323-332
[52]  
Klibi W., 2013, Prepositioning emergency supplies to support disaster relief : A stochastic programming approach
[53]  
Kongsomsaksakul S., 2005, J E ASIA SOC TRANSPO, V6, P4237
[54]   A hybrid model for learning from failures: The Hurricane Katrina disaster [J].
Labib, Ashraf ;
Read, Martin .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (21) :7869-7881
[55]   Nuclear power as a climate mitigation strategy - technology and proliferation risk [J].
Lehtveer, Mariliis ;
Hedenus, Fredrik .
JOURNAL OF RISK RESEARCH, 2015, 18 (03) :273-290
[56]  
Lidskog R, 2015, J RISK RES, V18
[57]  
Lin Y.-H., 2012, SOCIO-ECON PLAN SCI, V46, P112, DOI [10.1016/j.seps.2012.01.001, DOI 10.1016/J.SEPS.2012.01.001]
[58]   A decision-support tool for post-disaster debris operations [J].
Lorca, Alvaro ;
Celik, Melih ;
Ergun, Oezlem ;
Keskinocak, Pinar .
HUMANITARIAN TECHNOLOGY: SCIENCE, SYSTEMS AND GLOBAL IMPACT 2015, HUMTECH2015, 2015, 107 :154-167
[59]   How does accessibility to post-disaster relief compare between the aging and the general population? A spatial network optimization analysis of hurricane relief facility locations [J].
Marcelin, Jean Michael ;
Homer, Mark W. ;
Ozguven, Erman ;
Kocatepe, Ayberk .
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 2016, 15 :61-72
[60]   Stochastic optimization of medical supply location and distribution in disaster management [J].
Mete, Huseyin Onur ;
Zabinsky, Zelda B. .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2010, 126 (01) :76-84