A multi-objective relief chain location distribution model for urban disaster management

Barzinpour, Farnaz; Esmaeili, V.

doi:10.1007/s00170-013-5379-x

Cited by 135 publications

(65 citation statements)

References 19 publications

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“…In the case of complex, high-dimensional problems related to large areas, the current algorithms can take an excessive amount of time and computing power and have difficulty in finding the global optimal solution [44]. Consequently, some studies continue to develop advanced algorithms [114] or divide the large-scale regions into several subregions to easily solve the above problem using current optimization software, such as LINGO or CPLEX [45]. Modifying the existing models using the dimension reduction approach is another alternative way to improve the solution quality and is one of the main directions of future research on disaster shelter location-allocation problems.…”

Section: Discussionmentioning

confidence: 99%

“…To solve the more complex problems, multiobjective models, based on the p-median problem, the p-center problem, the covering problem, and so on, are developed. For example, Barzinpour and Esmaeili [45] developed the multiobjective mixed-integer linear programming model, with the objectives of maximizing the population coverage and minimizing the construction costs and traffic costs by using a virtual zoning approach to achieve humanitarian and financial goals. From the examination of the multiobjective site selection problems, the objectives, constraints, disaster types and methods for solutions associated with the natural disaster shelter location-allocation optimization model were summarized (Table 3).…”

Section: Multiobjective Modelmentioning

confidence: 99%

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Site Selection Models in Natural Disaster Shelters: A Review

Xü

Qin

et al. 2019

Sustainability

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Constructing natural disaster shelters is important for disaster emergency management, and site selection models provide a feasible technique and method. This paper presents site selection models for natural disaster shelters. A synthesis of the types, objectives, constraints, methods of solutions, targeted disasters and applications of different site selection models for natural disaster shelters is investigated. Shelter location models can be classified as single-objective models, multiobjective models and hierarchical models, according to the objective and hierarchy type. Minimizing the evacuation distance or time, shelter construction cost or number, and the total risk are the general objectives of the models. Intelligent optimization algorithms are widely used to solve the models, instead of the Geographic Information System (GIS) method, due to the complexity of the problem. The results indicate that the following should be the main focuses of future works: How to set a model that can be applied for determining the shelter locations of multiple disasters; how to consider the uncertainty in the models; how to improve the existing algorithms or models to solve large-scale location-allocation problems; and how to develop a new resource-saving model that is consistent with the concept of sustainable development, as advocated by shelter planners and policy makers, which can be applied in real situations. This study allows those undertaking shelter location research to situate their work within the context of shelter planning.

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Section: Discussionmentioning

confidence: 99%

Section: Multiobjective Modelmentioning

confidence: 99%

Site Selection Models in Natural Disaster Shelters: A Review

Xü

Qin

et al. 2019

Sustainability

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“…There are studies incorporating GIS in the analysis to manage the uncertainty of the impact of the disaster. Barzinpour and Esmaeili (2014) used the Risk Assessment tool for Diagnosis of Urban Areas against Seismic Disaster for different earthquake scenarios. The information supports a formulation including three objective functions seeking to maximise coverage, minimise location cost and minimise operational cost.…”

Section: Logistical Activities In Disaster Preparednessmentioning

confidence: 99%

Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods

Rodríguez-Espíndola

Albores

Brewster

2018

European Journal of Operational Research

160

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The logistical deployment of resources to provide relief to disaster victims and the appropriate planning of these activities are critical to reduce the suffering caused. Disaster management attracts many organisations working alongside each other and sharing resources to cope with an emergency.Consequently, successful operations rely heavily on the collaboration of different organisations.Despite this, there is little research considering the appropriate management of resources from multiple organisations, and none optimising the number of actors required to avoid shortages or convergence.This research introduces a disaster preparedness system based on a combination of multi-objective optimisation and geographical information systems to aid multi-organisational decision-making. A cartographic model is used to avoid the selection of floodable facilities, informing a bi-objective optimisation model used to determine the location of emergency facilities, stock prepositioning, resource allocation and relief distribution, along with the number of actors required to perform these activities.The real conditions of the flood of 2013 in Acapulco, Mexico, provided evidence of the inability of any single organisation to cope with the situation independently. Moreover, data collected showed the unavailability of enough resources to manage a disaster of that magnitude at the time. The results highlighted that the number of government organisations deployed to handle the situation was excessive, leading to high cost without achieving the best possible level of satisfaction. The system proposed showed the potential to achieve better performance in terms of cost and level of service than the approach currently employed by the authorities.Keywords: Humanitarian logistics, multiple objective programming, OR in disaster relief, disaster preparedness. THEORETICAL FRAMEWORK2.

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“…They considered two hierarchical objective functions that are concerned with minimizing transit times for both goods and the injured people. Barzinpour and Esmaeili (2014) [18] proposed a multi-objective mixedinteger linear programming model for preparation of disaster logistics scheduling based on demand area population and damage severity. They applied goalprogramming approach to prioritize objectives in order to face the least deviation from goals.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

Relief commodities distribution planning considering the features of demand areas: a robust multi-objective approach

2017

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Abstract. Logistics planning plays an important role in providing services to disasterstricken areas. In this study, a scenario-based multi-objective model is presented to locate distribution and evacuation centers and distribute relief commodities with appropriate allocation. It aims to serve the earthquake-stricken areas that are classi ed according to their construction qualities. The objective functions of cost, responsiveness, and demand coverage are considered in the proposed optimization model. Moreover, due to the uncertain nature of a disaster and uncertainty in some parameters of the model, a robust optimization approach is utilized. A revised multi-choice goal programming method is applied to solve the multi-objective model. The proposed model is validated through a case study conducted in the city of Amol. The computational results show the e ciency of the proposed model in a real-world disaster situation.

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A multi-objective relief chain location distribution model for urban disaster management

Cited by 135 publications

References 19 publications

Site Selection Models in Natural Disaster Shelters: A Review

Site Selection Models in Natural Disaster Shelters: A Review

Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods

Relief commodities distribution planning considering the features of demand areas: a robust multi-objective approach

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