Staff assignment policies for a mass casualty event queuing network

Abstract

We study parallel queuing systems in which heterogeneous teams collaborate to serve queues with three different prioritization levels in the context of a mass casualty event. We assume that the health condition of casualties deteriorate as time passes and aim to minimize total deprivation cost in the system. Servers (i.e. doctors and nurses) have random arrival rates and they are assigned to a queue as soon as they arrive. While nurses and doctors serve their dedicated queues, collaborative teams of doctors and nurses serve a third type of customer, the patients in critical condition. We model this queueing network with flexible resources as a discrete-time finite horizon stochastic dynamic programming problem and develop heuristic policies for it. Our results indicate that the standard $$c \mu $$ rule is not an optimal policy, and that the most effective heuristic policy found in our simulation study is intuitive and has a simple structure: assign doctor/nurse teams to clear the critical patient queue with a buffer of extra teams to anticipate future critical patients, and allocate the remaining servers among the other two queues.