Multiple criteria decision engineering to support management in military healthcare and logistics operations

The U.S. Department of Defense Military Health System (MHS) is a unique health system in that it recruits and trains its own medical staff, has a generally physically fit patient population, and is a closed, single-payer system. The unique mission of the MHS comes with its own set of healthcare and logistics challenges above and beyond those of a civilian US-based health system. At the surface, the MHS is charged with delivering quality healthcare to a diverse population. At the core, however, that charge includes maintaining peacetime healthcare delivery capacity while ensuring the deployment readiness of the active force, and deploying, establishing and running forward deployed healthcare facilities to provide the necessary health services support for combat, stability, peacekeeping, and humanitarian assistance operations. Further complicating the delivery of quality care is the transient nature of healthcare providers either due to deployments or routine personnel moves between hospitals, clinics, and field units.;As a result of these challenges, this doctoral dissertation employs methods of multiple criteria decision engineering to assist strategic decision-making and to support the complex planning and management of military healthcare resources, personnel, logistics, and financial incentives.;Multi-criteria and stochastic optimization models that leverage mixed-integer programming, Monte Carlo simulation, discrete event simulation, text mining, clustering analysis, regression modeling and econometrics are developed to provide critical insights for military decision-makers. The multiple criteria decision engineering methods in this dissertation are applied to several real-world decision problems within military healthcare and logistics operations to illustrate the impact and relevance of the results.;First, we proffer the Multi-Objective Auto-Optimization Model (MAOM) -- a resource allocation-based optimization model that adjusts resources (system inputs) automatically -- which provides decision-makers with a decision-support tool for re-allocating resources in large health systems that are centrally controlled and funded, such as the MHS. The necessity to efficiently balance and re-allocate system resources among hospitals in a hospital network is paramount, especially as health systems experience increasing demand and costs for health services.;Second, we proffer the Objective Force Model (OFM), a deterministic, mixed-integer linear weighted goal programming model to optimize workforce planning for the U.S. Army Medical Department (AMEDD) Personnel Proponency Directorate (APPD). We also develop two stochastic variants of the linear OFM, which incorporate probabilistic components associated with uncertain officer continuation rates. We employ a discrete event simulation model to verify and validate the results.;Third, we develop a multiple criteria decision analysis (MCDA) framework to optimize the military humanitarian assistance/disaster relief (HA/DR) aerial delivery supply chain network under uncertainty. The model uses stochastic, mixed-integer, weighted goal programming to optimize network design, logistics costs, staging locations, procurement amounts, and inventory levels. The MCDA framework enables decision-makers to explore the trade-offs between military HA/DR aerial delivery supply chain efficiency and responsiveness, while optimizing across a wide range of real-world, probabilistic scenarios to account for the inherent uncertainty in the location of global humanitarian disasters, as well as the amount of demand to be met.;Fourth, we propose the Fuzzy Multi-Objective Auto-Optimization Model (FMAOM), an optimization model with fuzzy constraints that can be used for automatic resource re-allocation with respect to different levels of risk preferences. The efficient use of resources in health systems is crucial mostly due to the increasing demand and limited funding.;Fifth, we measure the effect of a monetary incentive model on hospital efficiency and outcomes. The Army component of the MHS implemented a pay-for-performance financial incentive program in 2007 in an effort to stimulate patient quality, access, and satisfaction improvements. Using a retrospective, quasi-experimental design, the empirical analysis incorporates data envelopment analysis (DEA) with time windows and difference-in-differences estimation. Hospitals are evaluated in the U.S. Army, Air Force, and Navy during the period of 2001--2012. The results indicate a statistically significant reduction in efficiency for the hospitals that received financial incentives. The health policy implications of this study are applicable in light of the national healthcare debate and may assist healthcare policy-makers in determining the efficacy and associated trade-offs of pay-for-performance financing models.;Last, we introduce the Stochastic Multi-Objective Auto-Optimization Model (SMAOM) for resource allocation decision-making under uncertainty in the MHS. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The usefulness of the proposed model is illustrated by a computational experiment in which a traditional DEA model is compared to the proposed SMAOM for 128 hospitals in the three services (Air Force, Navy, Army) in the MHS using hospital-level data from 2009 - 2013. (Abstract shortened by ProQuest.).