In 2001, Congress enacted legislation that required a 2005 Base Realignment and Closure (BRAC) round to realign military units, remove excess facility capacity, and support defense transformation. The United States Army used multiple-objective decision analysis to determine the military value of installations and an installation portfolio model to develop the starting point to identify potential unit realignments and base closures, providing the basis for all recommendations. Ninety-five percent of the army’s recommendations were accepted by the BRAC 2005 Commission. The army expects these recommendations to create recurring savings of $1.5 billion annually after completion of BRAC implementation. This paper offers four contributions to decision analysis literature: an instructive application of multiple-objective decision analysis methods to portfolio selection, a useful method for constructing scales for interdependent attributes, a new method for assessing weights that explicitly considers importance and variation (Swing Weight Matrix), and practical advice on how to use multiple-objective decision analysis methods in a complex and controversial political environment.

There are over one million United States active-duty Army, Army National Guard, and Army Reserve soldiers. The Army assigns each soldier to a unit at one of over 4,000 worldwide locations; these facilities consist of approximately 15 million acres and 287 million square feet. The Army can change a soldier's unit assignment; it can also move a unit's home installation. This paper presents an integer linear program, Optimally Stationing Army Forces (OSAF), which prescribes optimal Army stationing for a given set of units. OSAF uses the existing starting locations, set of installations, available implementation dollars, and unit requirements for facilities, ranges, and maneuver land. It has provided the Army with stationing analysis for several years. Perhaps most significantly, OSAF helped with the closure and realignment decisions during the 2005 round of Base Realignment and Closure (BRAC). As a result of this BRAC, by 2011 the Army will close 400 installations (13 installations that primarily house active-duty soldiers, 176 Army Reserve centers, and 211 National Guard armories) and realign 56 active units. These BRAC actions will impact 43 states, cost more than $13 billion to implement, and generate an expected 20-year net savings of $7.6 billion.

We developed a system for managing inventory at Jeppesen Sanderson, Inc., a major provider of aviation-information products. The system determines order quantities for charts used in flight manuals. These charts contain essential safety information that changes frequently, making standard methods for inventory management ineffective. We formulated the problem as a dynamic programming model and developed a simple heuristic-solution procedure for determining order quantities. Based on this procedure, we also developed a decision support system (DSS) and implemented it for 600 of the most expensive Jeppesen charts. The system has been in use since August 1998, generating actual annual cost reductions of over $800,000.

Abstract:We examine several methods for evaluating resource acquisition decisions under uncertainty. Traditional methods may underestimate equipment benefit when part of this benefit comes from decision flexibility. We develop a new, practical method for resource planning under uncertainty, and show that this approach is more accurate than several commonly used methods. We successfully applied our approach to an investment problem faced by a major firm in the aviation information industry. Our recommendations were accepted and resulted in estimated annual savings in excess of $1 million (US).

Jeppesen Sanderson, Inc. maintains, manufactures, and distributes flight manuals containing safety information for over 300,000 pilots and 400 airlines worldwide. Its service deteriorated when a growing line of over 100,000 aviation charts overwhelmed its production system. We developed optimization-based decision support tools that improved production planning. Concurrently, we developed a method for evaluating investments in production technology. Our work reduced lateness and improved production processes, which led to a decrease in customer complaints, a reduction in costs of nearly 10 percent, an increase in profit of 24 percent, and the creation of a new OR group. Today, OR-based decision support systems are spreading to all areas of the company.