Optimising shelter location and evacuation routing operations: The critical issues

Amideo, Annunziata Esposito; Scaparra, Maria Paola; Kotiadis, Kathy

doi:10.1016/j.ejor.2018.12.009

Cited by 99 publications

(26 citation statements)

References 71 publications

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“…We considered the decisions that could be explored for COVID-19 by the modelling and simulation community in its four phases of mitigation; preparedness; response and recovery (Altay & Green, 2006;Amideo et al, 2019) but given the time at which we are writing the article, none of our decisions appear in the mitigation phase: this time has already passed. In places, we have identified decision areas where modelling could support the mitigation phase by considering developing models for stress testing of operational readiness.…”

Section: Developing Research and Tools To Support The Mitigation Phasementioning

confidence: 99%

How simulation modelling can help reduce the impact of COVID-19

Currie

Fowler

Kotiadis

et al. 2020

Journal of Simulation

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366

273

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Modelling has been used extensively by all national governments and the World Health Organisation in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community. ARTICLE HISTORY

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Section: Developing Research and Tools To Support The Mitigation Phasementioning

confidence: 99%

How simulation modelling can help reduce the impact of COVID-19

Currie

Fowler

Kotiadis

et al. 2020

Journal of Simulation

Self Cite

366

273

View full text Add to dashboard Cite

show abstract

“…These facilities include secure infrastructures and facilities that may be disrupted to ensure the smooth distribution of post-disaster relief supplies [26]. However, the issue of considering shelters that can provide different types of services to meet the needs of different asylum seekers has been largely ignored [27].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (26) restricts disaster victims of different needs can receive it. Constraint (27) ensures that each shelter can be associated with only one type. Constraints (28) and (29) ensure that only selected shelters can provide services for the victims.…”

Section: Multi-objective Optimization Model Constructionmentioning

confidence: 99%

Multi-Criteria Location Model of Emergency Shelters in Humanitarian Logistics

2020

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Natural disasters can cause serious casualties and economic losses, and emergency shelters are effective measures to reduce disaster risks and protect lives. At present, the location models of refuge facilities often ignore the diversion of shelter from the perspective of humanitarian logistics and the needs of victims. Such models also seldom consider the impact of the pre-storage of relief materials on the location of shelters. In this study, on the basis of the different needs of disaster victims, shelters are divided into two types—basic life and psychological medical service guarantees. While considering the full coverage of shelter needs, capacities, and budget constraints, the shelter distance, the optimized distribution of refugees, and the pre-stock quantity of goods are optimized. The facility service quality is optimized on the basis of qualitative factors. This study proposes a multi-standard constrained site selection model to optimize the pre-disaster shelter site-allocation problem. The model is helpful for decision makers to influence shelter siting and victims’ allocating process through their expertise and to obtain a solution that compromises multiple objectives. In this study, several basic cases are generated from the actual data of certain areas in Sichuan Province, a disaster-prone region in China, to verify the effectiveness of the model.

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“…For example, in Hamacher and Tjandra [19] two evacuation scenarios are considered, precautionary and life-saving operations. Furthermore, Amideo et al [27] stablished that hurricanes and wildfires require preventive evacuation while earthquakes and floods demand immediate post-disaster evacuation. Pyakurel et al [28] established that an evacuation optimizer looks for a plan on an evacuation network for an efficient transfer of the maximum quantity of evacuees from the dangerous points (sources) to safer ones (sinks) as quickly as possible.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…In terms of regional evacuation, and according to Amideo et al [27], the related literature prior to 2011 used to be focused on shelter location-allocation and self-evacuation. The first paper on regional supported (or bus-based) evacuation is [31], where this type of evacuation is presented as a variant of the vehicle routing problem.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Supported Evacuation for Disaster Relief through Lexicographic Goal Programming

et al. 2020

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Disasters have been striking human-beings from the beginning of history and their management is a global concern of the international community. Minimizing the impact and consequences of these disasters, both natural and human-made, involves many decision and logistic processes that should be optimized. A crucial logistic problem is the evacuation of the affected population, and the focus of this paper is the planning of supported evacuation of vulnerable people to safe places when necessary. A lexicographic goal programming model for supported evacuation is proposed, whose main novelties are the classification of potential evacuees according to their health condition, so that they can be treated accordingly; the introduction of dynamism regarding the arrival of potential evacuees to the pickup points, according to their own susceptibility about the disaster and the joint consideration of objectives such us number of evacuated people, operation time and cost, among which no trade-off is possible. The performance of the proposed model is evaluated through a realistic case study regarding the earthquake and tsunami that hit Palu (Indonesia) in September 2018.

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Optimising shelter location and evacuation routing operations: The critical issues

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 99 publications

References 71 publications

How simulation modelling can help reduce the impact of COVID-19

How simulation modelling can help reduce the impact of COVID-19

Multi-Criteria Location Model of Emergency Shelters in Humanitarian Logistics

Supported Evacuation for Disaster Relief through Lexicographic Goal Programming

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