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

MULTIPLE CRITERIA DECISION ENGINEERING TO SUPPORT MANAGEMENT IN MILITARY HEALTHCARE AND LOGISTICS OPERATIONS Nathaniel D. Bastian, MS, MEng, PEM PhD Candidate, NSF Graduate Research Fellow Department of Industrial & Manufacturing Engineering Pennsylvania State University 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. Due to the complexity of the MHS, effective management of healthcare resources and logistics is paramount. This is also important to senior military leaders since many real-world decision problems in the MHS exhibit the presence of multiple, conflicting objectives for judging alternatives, as well as the need for making compromises or trade-offs regarding the outcomes of alternate courses of action. Further, most of these same resource allocation decision problems are faced under inherent, underlying uncertainties. 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. The primary motivation is the requirement to provide data-driven, managerial decision support for decision problems in: 1) resource allocation and performance within the military hospital network, 2) military medical workforce planning and future force structure, 3) supply chain network design for humanitarian assistance and disaster relief aerial delivery operations, and 4) effectiveness of healthcare financial incentives on hospital efficiency. 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