A two-echelon stochastic facility location model for humanitarian relief logistics

Döyen, Alper; Aras, Necatı; Barbarosoğlu, Gülay

doi:10.1007/s11590-011-0421-0

Cited by 172 publications

(83 citation statements)

References 38 publications

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“…Two-stage programming with recourse was adopted to plan relief shelters and then the allocation of evacuees to shelters in Li et al (2011) in which the authors adopted a two-echelon network to represent both evacuee and resource flows to permanent and temporary shelters. In contrast, Döyen et al (2012) developed a two-echelon stochastic model for modelling flows between regional and local rescue centers, as well as locating them, prepositioning inventory, and then optimising cover and transport after the disaster, testing over many scenarios.…”

Section: Related Literaturementioning

confidence: 99%

Stochastic network models for logistics planning in disaster relief

Alem

Clark

Moreno

2016

European Journal of Operational Research

227

102

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Emergency logistics in disasters is fraught with planning and operational challenges, such as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable information about the location and needs of victims, possible random supplies and donations, precarious transport links, scarcity of resources, and so on. This paper develops a new two-stage stochastic network flow model to help decide how to rapidly supply humanitarian aid to victims of a disaster within this context. The model takes into account practical characteristics that have been neglected by the literature so far, such as budget allocation, fleet sizing of multiple types of vehicles, procurement, and varying lead times over a dynamic multiperiod horizon. Attempting to improve demand fulfillment policy, we present some extensions of the model via state-of-art risk measures, such as semideviation and conditional value-at-risk. A simple two-phase heuristic to solve the problem within a reasonable amount of computing time is also suggested.Numerical tests based on the floods and landslides in Rio de Janeiro state, Brazil, show that the model can help plan and organise relief to provide good service levels in most scenarios, and how this depends on the type of disaster and resources. Moreover, we demonstrate that our heuristic performs well for real and random instances.

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Section: Related Literaturementioning

confidence: 99%

Stochastic network models for logistics planning in disaster relief

Alem

Clark

Moreno

2016

European Journal of Operational Research

227

102

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“…The NGOs are obliged to preposition and store their items and equipment in the most suitable warehouse considering possible disaster areas. Most of the studies in literature present optimization models for warehouse location selection (Yushimito et al 2012;Jia et al 2007;Döyen et al 2012;Rawls, Turnquist 2010;Duran et al 2011;Gunnec, Salman 2007). Information systems are critical to the success or failure of humanitarian logistics (Marx 2009).…”

Section: The Critical Success Factors Of Hrlm Operationsmentioning

confidence: 99%

An assessment approach for non-governmental organizations in humanitarian relief logistics and an application in Turkey

Çelik

Gümüş

2015

Technological and Economic Development of Economy

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Abstract. The ever-increasing natural disasters have been causing the loss of lives, properties and resources. By the preparedness and response ability of non-governmental organizations, it is aimed to minimize these losses. In this paper, first, the critical success factors of humanitarian relief logistics management operations are determined and categorized. Then, by considering these factors, a hybrid method that consists of trapezoidal interval type-2 fuzzy sets, AHP and TOPSIS, is proposed to evaluate emergency preparedness and response ability performance of non-governmental relief organizations. The proposed hybrid method is applied for non-governmental relief organizations in Turkey to evaluate their performance, and to the factors need to be improved for each determined organization.

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“…Tomasini and Van Wassenhove (2009) investigated the evolution of supply chain management in disaster relief and the role of new players like the private sector. Döyen et al (2012) developed a two-stage stochastic programming technique for a humanitarian relief logistics problem in which decisions could be made for pre-and post-disaster rescue centers, the amount of relief items to be stored at the pre-disaster rescue centers, the amount of relief item flows at each echelon, and the amount of relief item shortage. Doerner and Hartl (2008) investigated various transportation problems associated with health care logistics, emergency preparedness and disaster relief.…”

Section: Introductionmentioning

confidence: 99%

A location-routing model on relief distribution centers

Padasht¹,

Razmi²

2016

10.5267/j.uscm

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There have been many unexpected natural disasters such as earthquake, flood, etc. in developing countries, which have created catastrophic incidents and we need to do appropriate planning for relief to reduce the possible casualties. Such actions normally face different challenges such as damages on transportation infrastructures including roads, bridges, etc. One of the primary actions for such crises management is associated with facility location for relief distribution centers. This paper presents a multi-objective mathematical problem and applies it for a real-world case study in northern region of Iran. The study uses Lp metric to handle different objectives and fuzzy programming is used to cope with uncertainty. The preliminary results indicate that the proposed study of this paper has been able to provide efficient results.Growing Science Ltd. All rights reserved. 6

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A two-echelon stochastic facility location model for humanitarian relief logistics

Cited by 172 publications

References 38 publications

Stochastic network models for logistics planning in disaster relief

Stochastic network models for logistics planning in disaster relief

An assessment approach for non-governmental organizations in humanitarian relief logistics and an application in Turkey

A location-routing model on relief distribution centers

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