This grant provides funding for the development of simple but effective decision rules for allocating scarce resources (such as ambulances and operating rooms) to patients in the aftermath of mass-casualty incidents. The problem formulations are based on the model of Sacco Triage Method (STM), which was proposed to obtain a good patient allocation policy in real-time by solving a linear program. This research will significantly expand on the model of STM by introducing stochastic components that are highly relevant in practice and also by placing it within a dynamic programming framework for a better understanding of decisions for dynamic patient triage. Instead of using the model to obtain a solution in real time, it will be used to investigate structural characteristics of optimal or near-optimal policies and also to develop heuristic methods when optimal policies cannot be characterized or are not easy to implement. A comprehensive simulation model will be developed, which will be used in testing the performances of the proposed policies and guidelines.
If successful, this research will provide scientific support for the emergency response community in their revived effort of developing effective emergency response policies. The main objective of this work is to identify effective but also simple rules so as to make them more appealing for practitioners. The research will identify broad guidelines that can be easily adopted in the field. These guidelines will essentially determine the type of the prioritization policy to be used depending on easily observable measures such as the approximate total number of casualties. The proposed work will also contribute to the classical job scheduling literature in Operations Research since it involves the analysis of a novel formulation for a job scheduling problem that arises in many different settings.
