Buildings and related civil infrastructure are an important factor of production that contribute directly to the accomplishment of an organization's mission and/or the generation of revenue. Aging, obsolescence, and general deterioration of these buildings, and their systems and components, can adversely affect the ability to accomplish a mission or generate expected revenue, thus resulting in an elevated risk profile. Maintenance, repair, and renovation (MR&R) activities, when planned effectively, can affect performance in such a way to reduce this risk. A rapidly aging infrastructure and building stock in the United States and across the world jeopardizes the ability to generate output and accomplish a mission at status quo. Moreover, rapidly expanding demands on some infrastructure will likewise make the status quo greatly inadequate in the near future. This requires two highly interrelated strategies: (1) to introduce new capabilities and capacities into the infrastructure stock to meet projected demand; and (2) to adequately manage, maintain, improve, and renew the existing infrastructure stock to slow performance degradation and fill demand gaps. The objective of this study is to develop a methodology for rapidly identifying and selecting multiyear building MR&R activities, such that facility performance is maximized and life cycle costs are minimized. This is a significant step toward the development of a comprehensive facility life cycle MR&R model that incorporates infrastructure economics and uncertainty for improved decision making. The result of this study is a model framework, to be applied against a building or group of buildings, which selects the optimum mixture of work activities considering condition, capability, performance, and life cycle costs. A genetic algorithm is employed to optimize the activity selection, and the proposed model approach is implemented against an example building to illustrate the methodology.
