Multi-Objective, Auto-Optimization Modeling for Resource Allocation Decision Making in the Military Health System.

摘要

As health systems continue to grow with increasing demand for health services, the necessity to efficiently balance resources among hospitals is paramount. This paper explicates the structural similarities between multiple objective programming and data envelopment analysis in order to proffer an original, hybrid resource allocation-based optimization model that adjusts resources (system inputs) either with or without decision-maker input. The motivation for this study is to develop a decision-support model to be used by health care managers and policymakers in support of resource allocations for large systems that are centrally controlled and funded, such as the Military Health System. In these systems, inputs are fixed at certain levels and may only be adjusted within Decision-Making Units (e.g., medical treatment facilities). We provide a mathematical formulation and example solutions based on both textbook and realworld data. We also find utility in the use of multi- start evolutionary algorithms to store multiple optimal solutions for consideration by decision-makers. This multi-objective, auto-optimization model is currently being used for the performance-based analysis of U.S. Army hospitals.