Managing a public agency's equipment fleet is rife with conflicting priorities. One of the most important aspects is the economic trade-off between the capital cost of replacing a piece of equipment and the ownership costs of operating and maintaining the machine in question if retained for another year. Therefore, determining life cycle costs and the economic life is vital for fleet managers to optimize equipment funds. Currently, most public agencies apply deterministic methods to make fleet management decisions. These methods do not account for uncertainty within the input parameters, such as volatility in fuel prices that potentially impact the replace-or-retain decision. Thus, the objective of this study is to develop a stochastic equipment life cycle cost analysis (LCCA) model to optimize equipment economic life based on life cycle costs for a public agency's fleet.

A public agency does not have financial flexibility; consequently, the constraints on the use of available funding can affect the replacement and repair cycles for its equipment fleet. Public sector financial constraints have the potential to put an agency's fleet into continuous decline if needed repairs cannot be made and old equipment cannot be replaced when it reaches the end of its economic life. This research will show that from the public perspective, there is a predisposition to retain a piece of equipment for as long as possible before replacing it because of the administrative burden required to get purchase authority. Thus, it is essential for the fleet manager to have a tool that will provide the accurate information to assist in making major equipment repair and replacement decisions. The public fund authorization process may require the agency to identify the need to replace a given piece of equipment a year or more in advance of the need, making the results of this research both timely and valuable for implementation.

The proposed stochastic equipment LCCA model is the result of a comprehensive literature review, national survey, case study analysis, and a software content analysis. Data from the Minneapolis Public Works Fleet Services Division (MPWFSD) was obtained during the case study analysis to utilize in this thesis. Also, a viable equipment LCCA model, the Peurifoy and Schexnayder model (PSM), was used in the analysis in addition to the use of engineering economics. The model utilizes stochastic inputs to quantify uncertainty and determine a given piece of equipment's optimum economic life.