A multi-objective combinatorial model of casualty processing in major incident response

Wilson, Duncan T.; Hawe, Glenn I.; Coates, Graham; Crouch, Roger S.

doi:10.1016/j.ejor.2013.04.040

Cited by 65 publications

(44 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The vast majority focuses on the location and allocation of emergency response units (Fiedrich et al, 2000) as well as on the supply and distribution of relief supplies (Barbarosoglu & Arda, 2004;Mete & Zabinsky, 2010). Few papers consider the transportation of casualties and flows of patients between locations (Wilson et al, 2013;Salman & Gul, 2014). Notably, many models assume that the same vehicles are used to distribute emergency supplies and simultaneously to transport casualties to treatment facilities (Yi & Kumar, 2007;Yi & Ozdamar, 2007;Ozdamar, 2011).…”

Section: Related Workmentioning

confidence: 99%

“…One way to capture the mortality rate is to consider the minimization of the (weighted) total flow time F w for all patients (Wilson et al, 2013). The later can be calculated as the sum of the (weighted) completion times of treatment of each patient.…”

Section: Hierarchical Objectivesmentioning

confidence: 99%

“…Considering that the patients can be seen as a set of jobs (see Wilson et al (2013) for a similar approach), the proposed model formulation follows the representation of a Flexible Job Shop Scheduling Problem (FJSP) (Rocha et al, 2008) with unrelated parallel machines and sequence-and machinedependent setup times. The FJSP consists of programming several jobs to be processed by several parallel identical and/or unrelated machines.…”

Section: Model Formulationmentioning

confidence: 99%

“…Recently, Wilson et al (2013) modeled the distribution of casualties to hospitals as a single-period multi-objective flexible job shop scheduling problem. Each patient is considered as a job and each responder unit as a ma-chine.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

Repoussis

Paraskevopoulos

Vazacopoulos

et al. 2016

European Journal of Operational Research

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThis paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. The goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIPEmail addresses: prepouss@stevens.edu (Panagiotis P. Repoussis), d.paraskevopoulos@bath.ac.uk (Dimitris C. Paraskevopoulos), alkis@optimizationdirect.com (Alkiviadis Vazacopoulos), nah2005@med.cornell.edu (Nathaniel Hupert) Preprint submitted to European Journal of Operational ResearchMay 25, 2016 based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Hierarchical Objectivesmentioning

confidence: 99%

Section: Model Formulationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

Repoussis

Paraskevopoulos

Vazacopoulos

et al. 2016

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…A humanitarian supply chain consists of four phases: mitigation, preparedness, response and recovery [9]. The mitigation phase includes actions performed to reduce the severity of a disaster [10,11]; the preparedness phase consists of activities that increase a community's ability to respond in case a disaster occurs [12][13][14][15][16]; the response phase addresses immediate threats after a disaster [17][18][19]; and the recovery phase consists of restoring the infrastructure to return a community to a near-normal condition [20,21]. According to Altay and Green [22], among all four phases of the humanitarian supply chain, the recovery phase is the area that is in dire need of more research.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated Location-Allocation Model for Temporary Disaster Debris Management under an Uncertain Environment

Habib

Sarkar

2017

Sustainability

View full text Add to dashboard Cite

Natural disasters always generate an overwhelming amount of debris. Reusing and recycling waste from disasters are essential for sustainable debris management. Before recycling the debris, it is necessary to sort this mixed waste. To perform the sorting process efficiently, a Temporary Disaster Debris Management Site (TDDMS) is required, and the selection of TDDMS is a multi-criteria decision-making problem due to its numerous regional and municipal constraints. This paper provides a two-phase framework for sustainable debris management during the response phase of disasters. In the first phase, a methodology for TDDMS selection is proposed that consists of Analytical Network Process (ANP) and a fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In the second phase, a debris allocation optimization model is developed to allocate the debris from disaster-affected regions to the selected TDDMS. A city prone to hurricane damage is selected to illustrate the proposed framework. For the debris allocation purpose, five TDDMS are chosen, among which three sites are selected using the proposed methodology. To illustrate the utilization of the proposed study, a numerical example with two different scenarios is provided. Numerical outcomes prove that the model results in a sustainable debris management system for disasters.

show abstract

A Decision Support Method for Prioritizing Investments Subject to Uncertainties

Thekdi

Aven

2017

Knowledge in Risk Assessment and Management

View full text Add to dashboard Cite

A multi-objective combinatorial model of casualty processing in major incident response

Cited by 65 publications

References 40 publications

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

An Integrated Location-Allocation Model for Temporary Disaster Debris Management under an Uncertain Environment

A Decision Support Method for Prioritizing Investments Subject to Uncertainties

Contact Info

Product

Resources

About