Pre-positioning disaster response facilities at safe locations: An evaluation of deterministic and stochastic modeling approaches

Verma, Anurag; Gaukler, Gary M.

doi:10.1016/j.cor.2014.10.006

Cited by 82 publications

(34 citation statements)

References 26 publications

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“…Researchers have realized the necessity of incorporating the stochastic nature of disasters into planning approaches and therefore deterministic models are only used as reference models to show their inadequateness, e.g. Verma and Gaukler (2014).…”

Section: Discussionmentioning

confidence: 99%

“…This modified L-shaped procedure is repeated until the optimal objective value of the overall problem is obtained. A case study for earthquakes in California is presented by Verma and Gaukler (2014) containing 20 demand points, 58 potential facility locations of three different sizes and up to three facilities to be opened. Note that despite this relatively small problem size, the corresponding CPU times exceed several hours, i.e.…”

Section: Decomposition Methodsmentioning

confidence: 99%

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Logistics Management

2016

Lecture Notes in Logistics

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Section: Discussionmentioning

confidence: 99%

Section: Decomposition Methodsmentioning

confidence: 99%

Logistics Management

2016

Lecture Notes in Logistics

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“…2014;Na ve Banerjee, 2015), stoğun önceden konumlandırılması (Lee vd. 2014;Opit ve Nakade, 2016), kuruluş yeri seçimi (Salman ve Yücel, 2015;Verma ve Bu çalışmalar genel olarak incelendiğinde; afetin türüne ve oluşturacağı olası hasarlara göre depo kuruluş yeri seçim kriterlerinin farklılaştığı görülmektedir. Bununla birlikte konum (ulaşım), maliyet ve altyapı sık kullanılan kriterler olarak dikkat çekmektedir.…”

Section: Li̇teratür Araştirmasiunclassified

Multi-criteria decision analysis model for warehouse location in disaster logistics

Ofluoglu¹,

Baki²,

Ar³

2017

Pressacademia

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To cite this document ABSTRACTPurpose-Optimum location of warehouses for disaster logistics increases the performance of humanitarian logistics, so that all the needs of beneficiaries can be delivered in a short time. In this paper, a multi-criteria decision analysis model is designed to decide the best warehouse location in disaster logistics in Trabzon, Turkey. Methodology-Entropy is used to determine the important weights of the criteria. The SAW, TOPSIS and VIKOR methods are utilized to rank the warehouse location alternatives. The ranking results are also combined by the Borda Count method to obtain a final ranking. The robustness of the model is examined by the sensitivity analysis. Findings-According to the results of the analysis, the disaster situation of the land, land size, and the distance of settlement were determined as the three most important criteria. Sensitivity analysis also proved the robustness of the model. Conclusion-The results are consistent with the expectations of the decision makers and seem to support their decisions. For future studies, the results can be compared using fuzzy techniques.Keywords: Disaster logistics, warehouse location decision, multicriteria decision making techniques. JEL Codes: C44, L81, Q54 AFET DEPO YERİ SEÇİMİ İÇİN ÇOK KRİTERLİ KARAR ANALİZİ MODELİ ÖZETAmaç-Afet lojistiği kapsamında depoların en uygun yerlere kurulması, insani yardım lojistiğinin performansını artırarak afetzedeler için gerekli tüm malzemelerin kısa bir sürede ihtiyaç yerlerine ulaştırılmasını sağlamaktadır. Bu çalışmada, Trabzon'da afet lojistiği kapsamında en uygun depo yerinin belirlenmesi amacıyla bir çok kriterli karar analizi modeli tasarlanmıştır. Yöntem-Bu model kapsamında ilk olarak Entropi Tekniği yardımıyla kriter ağırlıkları hesaplanmış ve ardından alternatif depo yerleri, çok kriterli karar verme teknikleri olan SAW, TOPSIS ve VIKOR kullanılarak sıralanmıştır. Elde edilen sonuçlar, Borda Sayım yöntemi kullanılarak bütünleşik bir sıralama elde edilmiştir. Ayrıca, son aşamada gerçekleştirilen duyarlılık analizi ile modelinin uygunluğu incelenmiştir. Bulgular-Analiz sonuçlarına göre arazinin afetselliği, arazi büyüklüğü ve yerleşim yerine uzaklık kriterleri en önemli üç kriter olarak tespit edilmiştir. Ayrıca, duyarlılık analizi ile de modelin uygunluğu ortaya konmuştur. Sonuç-Çalışmada elde edilen sonuçların karar vericilerin beklentileri ile örtüştüğü ve kararlarını desteklediği görülmektedir. Sonuçlar, gelecekte çalışmalarda bulanık yöntemler kullanılarak elde edilecek sonuçlar ile karşılaştırılabilir.Anahtar Kelimeler: Afet lojistiği, depo yer seçimi, çok kriterli karar verme teknikleri.

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“…Kulshrestha et al [7] utilize an uncertainty set to represent deviation of evacuation demand from the nominal value and propose a robust shelter location model. Verma and Gaukler [8] employ a stochastic programming model to locate disaster response facilities and obtain a cost advantage compared to deterministic models. However, these models do not consider the factors that affect shelter demand and do not reflect spatial and temporal variations in demand.…”

Section: Shelter Demand Estimationmentioning

confidence: 99%

Planning Emergency Shelters for Urban Disaster Resilience: An Integrated Location-Allocation Modeling Approach

Zhao

Sun

et al. 2017

Sustainability

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Abstract:In recent years, extreme natural hazards threaten cities more than ever due to contemporary society's high vulnerability in cities. Hence, local governments need to implement risk mitigation and disaster operation management to enhance disaster resilience in cities. Transforming existing open spaces within cities into emergency shelters is an effective method of providing essential life support and an agent of recovery in the wake of disasters. Emergency shelters planning must identify suitable locations for shelters and reasonably allocate evacuees to those shelters. In this paper, we first consider both the buildings' post-disaster condition and the human choice factor that affect evacuees' decision, and propose a forecasting method to estimate the time-varying shelter demand. Then we formulate an integrated location-allocation model that is used sequentially: an emergency shelter location model to satisfy the time-varying shelter demand in a given urban area with a goal of minimizing the total setup cost of locating the shelters and an allocation model that allocates the evacuees to shelters with a goal of minimizing their total evacuation distance. We also develop an efficient algorithm to solve the model. Finally, we propose an emergency shelters planning based on a case study of Shanghai, China.

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Pre-positioning disaster response facilities at safe locations: An evaluation of deterministic and stochastic modeling approaches

Cited by 82 publications

References 26 publications

Logistics Management

Logistics Management

Multi-criteria decision analysis model for warehouse location in disaster logistics

Planning Emergency Shelters for Urban Disaster Resilience: An Integrated Location-Allocation Modeling Approach

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