Stochastic optimization of medical supply location and distribution in disaster management

Mete, Huseyin Onur; Zabinsky, Zelda B.

doi:10.1016/j.ijpe.2009.10.004

Cited by 576 publications

(274 citation statements)

References 19 publications

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“…Whilst the health of casualties is not modelled explicitly, it could be argued that the demand for first aid resources at any one location within the disaster area includes implicit information regarding the health of the corresponding casualties. This is also the case for the resource distribution model proposed by Mete and Zabinsky (2010), which aims to satisfy the demand for resources at a number of hospitals. Casualties are included explicitly by Barbarosoglu et al (2002), who propose a model to determine how helicopters should be employed to transport casualties from a number of locations to hospital.…”

Section: No Health Level Distinctionmentioning

confidence: 98%

Modeling Uncertain and Dynamic Casualty Health in Optimization-Based Decision Support for Mass Casualty Incident Response

Wilson

Hawe

Coates

et al. 2013

International Journal of Information Systems for Crisis Response and Management

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Further information on publisher's website:http://dx.doi.org/10. 4018/jiscrm.2013040103 Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

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Section: No Health Level Distinctionmentioning

confidence: 98%

Modeling Uncertain and Dynamic Casualty Health in Optimization-Based Decision Support for Mass Casualty Incident Response

Wilson

Hawe

Coates

et al. 2013

International Journal of Information Systems for Crisis Response and Management

View full text Add to dashboard Cite

show abstract

“…Mete and Zabinsky (2010) [12] considered the cost and demand uncertainty and presented a stochastic model for the distribution and storage of medical products for managing a possible disaster. In their model, the location of warehouses and inventory levels were considered, too.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

“…Hence, di erent regions with di erent building resistance quality and demand under uncertainty are considered. [12]Moreover, the following question needs to be answered. Can we avoid (or at least minimize) damages of an earthquake by means of planning for it before its occurrence?…”

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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“…Skills are also considered by modeling them as team-task mismatch costs in the objective function. Barbarosoǧlu and Arda (2004), Beraldi et al (2004), Yi and Kumar (2007), and Mete and Zabinsky (2010), among others, apply stochastic programming techniques to the management of disasters of various natures. These studies, however, do not necessarily belong to the assignment problem realm.…”

Section: Literature Reviewmentioning

confidence: 99%

Management of Emergency Response Teams under Stochastic Demands

Markov

Varone

2013

Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems

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Abstract:We propose a stochastic optimization model for the composition of emergency response teams. An emergency intervention requires first an evaluation of the situation which results in the need of different skills. People involved in the response team must therefore comply with the required skills, be available, with a past and future workload respecting contractual compliance. In addition, we must also anticipate the possibility of future interventions that will require rare skills. It is the uncertain future demand for these skills that introduces stochasticities to the system. Since shifting agents between emergencies may be impossible or impractical, we would like to ensure that rare skills are not wasted but assigned to the emergency that most needs them. We model this with a mixed integer linear program implemented in AMPL and capable of being solved in real-time on common solvers.

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Stochastic optimization of medical supply location and distribution in disaster management

Cited by 576 publications

References 19 publications

Modeling Uncertain and Dynamic Casualty Health in Optimization-Based Decision Support for Mass Casualty Incident Response

Modeling Uncertain and Dynamic Casualty Health in Optimization-Based Decision Support for Mass Casualty Incident Response

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

Management of Emergency Response Teams under Stochastic Demands

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