Location and capacity allocations decisions to mitigate the impacts of unexpected disasters

Paul, Jomon Aliyas; MacDonald, Leo

doi:10.1016/j.ejor.2015.10.028

Cited by 103 publications

(50 citation statements)

References 23 publications

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“…e first stage included preposition of relief goods between storage areas according to forecasts, and the second stage was the response phase that includes distribution of relief goods between storage points and demand points. Paul and MacDonald [24] developed a stochastic model to determine the location and capacity of distribution centers in order to mitigate the impacts of disasters with (almost) no forewarning such as earthquakes. e objective function in the model was to minimize the sum of expected fatality costs, supply costs and the costs of building distribution centers.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Stochastic Model for an Earthquake Relief Network

Yenice

Samanlıoğlu

2020

Journal of Advanced Transportation

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Earthquake relief network involves storage and distribution of relief aid to people in need. In this paper, a new stochastic multi-objective mixed integer mathematical model is developed and implemented in Kadikoy municipality of Istanbul, Turkey in order to configure part of the earthquake relief network. The aim of the model is to help decision makers decide on the locations of storage areas for shelters pre-earthquake and distribution of shelters from these areas to temporary shelter areas post-earthquake while minimizing earthquake scenario-specific total expected distribution distance, total expected earthquake damage risk factor of storage areas and expected total penalty cost related to unsatisfied demand at temporary shelter areas, simultaneously. In the model, storage area capacity and coverage distance restrictions are taken into consideration. The data related to potential storage areas and shelter locations were obtained from Kadikoy municipality of Istanbul and Istanbul Metropolitan Municipality (IMM). The earthquake damage risk factors were determined based on possible earthquake scenarios given in Japan International Cooperation Agency’s (JICA) report. Four event scenarios with two different earthquake scenario likelihoods were considered and sample efficient solutions from the Pareto frontier were obtained implementing the normalized (scaled) weighted sum method.

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Section: Introductionmentioning

confidence: 99%

A Multi-Objective Stochastic Model for an Earthquake Relief Network

Yenice

Samanlıoğlu

2020

Journal of Advanced Transportation

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“…Such assumption may be too strict, especially for suddenly-occurring disasters like earthquakes. Actually, besides the uncertain demand, each disaster usually contains multiple sources of uncertainty, such as the magnitude of the disaster, the number of casualties, and the availability of rescue facilities, which should be considered when we design an efficient emergency plan [10]. It is worth noting that these factors are mutually interdependent.…”

Section: Introductionmentioning

confidence: 99%

Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters

Chen

Wang

2019

Discrete Dynamics in Nature and Society

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This paper is concerned with the optimal decisions of blood banks in a blood logistics network (BLN) with the consideration of natural disasters. One of the biggest challenges is how to deal with unexpected disasters. Our idea is to consider the disasters as the natural consequences of interaction among multiple interdependent uncertain factors, such as the locations and the levels of disasters, the number of casualties, and the availabilities of rescue facilities, which work together to influence the rescue effects of the BLN. Thus, taking earthquakes as the example, a Bayesian Network is proposed to describe such uncertainties and interdependences and, then, we incorporate it into a dedicated two-stage multi-period stochastic programming model for the BLN. The planning stage in the model focuses on blood bank location and inventory decisions. The subsequent operational stage is composed of multiple periods, some of which may suffer disasters and initiate corresponding rescue operations. Numerical tests show that the proposed approach can be efficiently applied in blood management under the complicated disaster scenarios.

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“…Fritz Institute [1] reported that lack of a system for monitoring and managing the relief aids in 2004 Southeast Asia tsunami led to large-scale shortages of relief items and medical personnel. The report discussed the main problems of the management of HRCs that motivated recent research studies to focus on facility location [2], relief items' allocation [3], and relief distribution/transportation issues [4]. These problems in the context of humanitarian supply chains are different from their counterparts in commercial supply chains in which, timeliness and effectiveness of relief operations are more important factors than cost [5].…”

Section: Introductionmentioning

confidence: 99%

“…Passed time after earthquake (hours); 2 : Distributed relief items into the affected area (million);3 : The number of needed transportation mode e between CT and LT, In the first 12 hours, the average number of LTs that were setup by existing configuration is more than or equal to the number of LTs that was set up by the HDSS. Although, more LTs have been setup in the existing configuration in the first 12 hours, the RS and BT measures are comparable to HDSS configuration.…”

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confidence: 99%

A hybrid decision support system for managing humanitarian relief chains

Sahebjamnia

Torabi

Mansouri

2017

Decision Support Systems

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Decisions regarding location, allocation and distribution of relief items are among the main concerns of the Humanitarian Relief Chain (HRC) managers in response to no-notice large-scale disasters such as earthquakes.In this paper, a Hybrid Decision Support System (HDSS) consisting of a simulator, a rule-based inference engine, and a knowledge-based system (KBS) is developed to configure a three level HRC. Three main performance measures including the coverage, total cost, and response time are considered to make an explicit trade-off analysis between cost efficiency and responsiveness of the designed HRC. In the first step, the simulator calculates the performance measures of the different configurations of the HRC under generated number of disaster scenarios. Then, the rule-based inference engine attempts to build the best configuration of the HRC including facilities' locations, relief items' allocation and distribution plan of the scenario under investigation based on calculated performance measures. Finally, the best configuration for each scenario is stored in the KBS as the extracted knowledge from the above analyses. In this way, the HRC managers can retrieve the most appropriate HRC configuration in accordance with the realized post-disaster scenario in an effective and timely manner. The results of a real case study in Tehran demonstrate that the developed HDSS is an effective tool for fast configuration of HRCs using stochastic data.

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Location and capacity allocations decisions to mitigate the impacts of unexpected disasters

Cited by 103 publications

References 23 publications

A Multi-Objective Stochastic Model for an Earthquake Relief Network

A Multi-Objective Stochastic Model for an Earthquake Relief Network

Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters

A hybrid decision support system for managing humanitarian relief chains

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