A dynamic simulation –optimization approach for managing mass casualty incidents

We present a generic method consisting of (i) an automated policy for dynamic staff re-allocation at an AMP with arbitrary structure, and (ii) a simulation–optimization approach for optimally parametrizing this automated policy. Three simulation–optimization techniques with two complexity levels are investigated in detail for the purpose of incorporation in our system applied to the Austrian AMP case study: the method by Kiefer–Wolfowitz, the metaheuristic OptQuest approach, and the Response Surface Methodology. Our results show that the optimized automated policies can improve the performance of the AMP compared to the management by simple heuristics or by human decision makers. We discuss policy implications for improving strategic decision making and process management for incident commanders at the Austrian AMP based on the results of the dynamic simulation–optimization techniques.
Source: Operations Research for Health Care - Category: Hospital Management Source Type: research