Robust, multi-objective optimization for the military medical evacuation location-allocation problem

Abstract

Determining where to locate mobile aeromedical staging facilities (MASFs) as well as identifying how many aeromedical helicopters to allocate to each MASF, commonly referred to as the medical evacuation (MEDEVAC) location-allocation problem, is vital to the success of a deployed MEDEVAC system. Within this research, we develop an integer mathematical programming formulation to determine the location and allocation of MEDEVAC assets over the phases of a military deployment to support operations ranging from peacekeeping through combat to post-combat resolution. Our model seeks to address the multi-objective problem of maximizing the expected demand coverage as a measure of solution effectiveness, minimizing the maximum number of located MASFs in any deployment phase as a measure of solution efficiency, and minimizing the total number of MASF relocations throughout the deployment as a measure of solution robustness. This research makes two contributions. First, it formulates a representative mathematical programming formulation and identifies an accompanying solution methodology (i.e., the ε-constraint Method) to assess and recommend improvements to deployed military MEDEVAC systems designed to provide large-scale emergency medical response for contingency operations that range in casualty-inducing intensity (i.e., demand) over the phases of a deployment. Second, the research illustrates the application of the model for a realistic, synthetically generated medical planning scenario in southern Azerbaijan. Comparisons are made between the model’s (multi-phase) optimal solution and the phase-specific optimal solutions that disregard concerns of transitions between phases. The results highlight the conflicting nature between the objectives and illustrate the trade-offs between objectives as restrictions applied to the second and third objectives are respectively tightened or relaxed.