Minimizing mortality in a mass casualty event: Fluid networks in support of modeling and staffing

Izack Cohen, Avishai Mandelbaum, Noa Zychlinski

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The demand for medical treatment of casualties in mass casualty events (MCEs) exceeds resource supply. A key requirement in the management of such tragic but frequent events is thus the efficient allocation of scarce resources. This article develops a mathematical fluid model that captures the operational performance of a hospital during an MCE. The problem is how to allocate the surgeons - the scarcest of resources - between two treatment stations in order to minimize mortality. A focus is placed on casualties in need of immediate care. To this end, optimization problems are developed that are solved by combining theory with numerical analysis. This approach yields structural results that create optimal or near-optimal resource allocation policies. The results give rise to two types of policies, one that prioritizes a single treatment station throughout the MCE and a second policy in which the allocation priority changes. The approach can be implemented when preparing for MCEs and also during their real-time management when future decisions are based on current available information. The results of experiments, based on the outline of real MCEs, demonstrate that the proposed approach provides decision support tools, which are both useful and implementable.

Original languageEnglish
Pages (from-to)728-741
Number of pages14
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number7
StatePublished - 3 Jul 2014
Externally publishedYes

Bibliographical note

Funding Information:
The work of A.M. has been partially supported by BSF grants 2005175 and 2008480, ISF grant 1357/08, and the Technion funds for promotion of research and sponsored research. Some of the research was funded by and carried out while A.M. was visiting the Statistics and Applied Mathematical Sciences Institute (SAMSI) of the NSF; the Department of Statistics and Operations Research (STOR), the University of North Carolina at Chapel Hill; the Department of Information, Operations and Management Sciences (IOMS), Leonard N. Stern School of Business, New York University; and the Department of Statistics, The Wharton School, University of Pennsylvania. The wonderful hospitality of these institutions is gratefully acknowledged and truly appreciated.


  • Mass casualty events
  • fluid models
  • optimal policy
  • resource allocation


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