Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Industrial Engineering

Committee Chair/Advisor

Mason, Scott J.

Committee Member

Ferrell, William G.

Committee Member

Taaffe, Kevin M.

Committee Member

Mayorga, Maria E.


Emergency medical service (EMS) systems provide medical care to pre-hospital patients who need rapid response and transportation. This dissertation proposes a new realistic approach for EMS systems in two major focuses: multiple unit dispatching and relocation strategies. This work makes recommendations for multiple-unit dispatch to multiple call priorities based on simulation optimization and heuristics. The objective is to maximize the expected survival rate. Simulation models are proposed to determine the optimization. A heuristic algorithm is developed for large-scale problems. Numerical results show that dispatching while considering call priorities, rather than always dispatching the closest medical units, could improve the effectiveness of EMS systems. Additionally, we extend the model of multiple-unit dispatch to examine fairness between call priorities. We consider the potentially-life-threatening calls which could be upgraded to life-threatening. We formulate the fairness problem as an integer programming model solved using simulation optimization. Taking into account fairness between priorities improves the performance of EMS systems while still operating at high efficiency. As another focus, we consider dynamic relocation strategy using a nested-compliance table policy. For each state of the EMS systems, a decision must be made regarding exactly which ambulances will be allocated to which stations. We determine the optimal nested-compliance table in order to maximize the expected coverage, in the binary sense, as will be later discussed. We formulate the nested-compliance table model as an integer program, for which we approximate the steady-state probabilities of EMS system to use as parameters to our model. Simulation is used to investigate the performance of the model and to compare the results to a static policy based on the adjusted maximum expected covering location problem (AMEXCLP). Additionally, we extend the nested-compliance table model to consider an upper bound on relocation time. We analyze the decision regarding how to partition the service area into smaller sub-areas (districts) in which each sub-area operates independently under separate relocation strategies. We embed the nested-compliance table model into a tabu search heuristic algorithm. Iteration is used to search for a near-optimal solution. The performance of the tabu search heuristic and AMEXCLP are compared in terms of the realized expected coverage of EMS systems.



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