Journal ArticleDOI
Stochastic optimization of medical supply location and distribution in disaster management
TLDR
A stochastic optimization approach for the storage and distribution problem of medical supplies to be used for disaster management under a wide variety of possible disaster types and magnitudes and can aid interdisciplinary agencies to both prepare and respond to disasters by considering the risk in an efficient manner.About:
This article is published in International Journal of Production Economics.The article was published on 2010-07-01. It has received 623 citations till now. The article focuses on the topics: Emergency management & Stochastic programming.read more
Citations
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Journal ArticleDOI
Optimization models in emergency logistics: A literature review
TL;DR: Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics and identifies research gaps identified and future research directions are proposed.
Journal ArticleDOI
A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty
TL;DR: The proposed multi-objective robust stochastic programming approach for disaster relief logistics under uncertainty can help in making decisions on both facility location and resource allocation in cases of disaster relief efforts.
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Models, solutions and enabling technologies in humanitarian logistics
Linet Özdamar,Mustafa Alp Ertem +1 more
TL;DR: In this review, information systems applications in humanitarian logistics are also surveyed, since humanitarian logistics models and their solutions need to be integrated with information technology to enable their use in practice.
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A survey of healthcare facility location
TL;DR: A framework to classify different types of non-emergency and emergency HCFs in terms of location management is presented, and the literature based on the framework is reviewed and future research possibilities are analyzed.
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Humanitarian logistics network design under mixed uncertainty
TL;DR: Computational results using real data reveal promising performance of the proposed SBPSP model in comparison with the existing relief network in Tehran and contributes to the literature on optimization based design of relief networks under mixed possibilistic-stochastic uncertainty.
References
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Journal ArticleDOI
Facility Location Under Uncertainty: A Review
TL;DR: A review of the literature on stochastic and robust facility location models can be found in this article, where the authors illustrate both the rich variety of approaches for optimization under uncertainty and their application to facility location problems.
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Emergency Logistics Planning in Natural Disasters
TL;DR: A planning model that is to be integrated into a natural disaster logistics Decision Support System is developed that addresses the dynamic time-dependent transportation problem that needs to be solved repetitively at given time intervals during ongoing aid delivery.
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A two-stage stochastic programming framework for transportation planning in disaster response
Gülay Barbarosoğlu,Yasemin Arda +1 more
TL;DR: This study proposes a two-stage stochastic programming model to plan the transportation of vital first-aid commodities to disaster-affected areas during emergency response, and a multi-commodity, multi-modal network flow formulation is developed to describe the flow of material over an urban transportation network.
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A dynamic logistics coordination model for evacuation and support in disaster response activities
Wei Yi,Linet Özdamar +1 more
TL;DR: The proposed model is a mixed integer multi-commodity network flow model that treats vehicles as integer commodity flows rather than binary variables that results in a more compact formulation whose output is processed to extract a detailed vehicle route and load instruction sheet.
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Ambulance location and relocation models
TL;DR: This article traces the evolution of ambulance location and relocation models proposed over the past 30 years and describes the models classified in two main categories: deterministic and dynamic.