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Casualty Collection Points Optimization: A Study for the District of Columbia

Author

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  • Aruna Apte

    (Graduate School of Business and Public Policy, Naval Postgraduate School, Monterey, California 93943)

  • Curtis Heidtke

    (Office of Counsel, Naval Postgraduate School, Monterey, California 93943)

  • Javier Salmerón

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

Abstract

A casualty collection point (CCP) is a single, predetermined location that is organized, staffed, and equipped to provide decontamination (if required), emergency medical assessment, treatment, and, where necessary, onward transportation of victims of a mass casualty incident. Emergency planners in the District of Columbia have recognized the desirability of developing a tool to assist planners in selecting CCPs within the affected area following a major incident. We develop a CCP optimization model (CCPOM) that provides planners and policymakers with strategic and operational insights into the complex problem of selecting optimal CCP locations to maximize casualty throughput for a range of incident parameters. Even more relevant, the CCPOM determines the utilization of personnel, decontamination units, and ambulances, providing planners with a general structure for resource allocation and signaling shortfalls that may lead to bottlenecks in casualty processing at the CCPs. District planners found many nonintuitive CCPOM results to be significant to their planning, programming, and budgeting efforts, and now consider the model's categorized resource utilization to be an integral part in updating District plans for both national special security event planning and everyday events.

Suggested Citation

  • Aruna Apte & Curtis Heidtke & Javier Salmerón, 2015. "Casualty Collection Points Optimization: A Study for the District of Columbia," Interfaces, INFORMS, vol. 45(2), pages 149-165, April.
    • Handle: RePEc:inm:orinte:v:45:y:2015:i:2:p:149-165
    DOI: 10.1287/inte.2014.0757
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    Cited by:

    1. Chang, Kuo-Hao & Chen, Tzu-Li & Yang, Fu-Hao & Chang, Tzu-Yin, 2023. "Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1237-1262.
    2. Muhammad Salman Habib & Biswajit Sarkar, 2017. "An Integrated Location-Allocation Model for Temporary Disaster Debris Management under an Uncertain Environment," Sustainability, MDPI, vol. 9(5), pages 1-26, April.
    3. Yiping Jiang & Yufei Yuan, 2019. "Emergency Logistics in a Large-Scale Disaster Context: Achievements and Challenges," IJERPH, MDPI, vol. 16(5), pages 1-23, March.
    4. Areej Alhothali & Budoor Alwated & Kamil Faisal & Sultanah Alshammari & Reem Alotaibi & Nusaybah Alghanmi & Omaimah Bamasag & Manal Bin Yamin, 2022. "Location-Allocation Model to Improve the Distribution of COVID-19 Vaccine Centers in Jeddah City, Saudi Arabia," IJERPH, MDPI, vol. 19(14), pages 1-21, July.
    5. Azrah A. Anparasan & Miguel A. Lejeune, 2018. "Data laboratory for supply chain response models during epidemic outbreaks," Annals of Operations Research, Springer, vol. 270(1), pages 53-64, November.
    6. Azrah Anparasan & Miguel Lejeune, 2019. "Resource deployment and donation allocation for epidemic outbreaks," Annals of Operations Research, Springer, vol. 283(1), pages 9-32, December.

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