End-of-life asset planning and management with retrofitting
This study develops a reverse logistics network design for end-of-life (EOL) use of assets and asset management strategies with a retrofit for existing assets. The models specifically incorporate different EOL options for wind turbines (WTs) before their useful life. The reverse logistics network model and asset management model allow decision makers to accurately compute the trade-off between long-term costs and capital costs as a function of reliability/availability and maintenance in order to determine optimal decision strategies. These modeling endeavors are motivated by overwhelming evidence in the literature, which has clearly shown that operation and maintenance (O&M) costs increase as assets age. This has implications for an asset manager’s decision of when one should buy, use, retrofit or sell assets to minimize total operating and maintenance costs. Unfortunately, at some point in time, a significant number of WTs will reach the end of their service life. In this study, we address this deficiency by first using mixed-integer linear programming (MILP) to develop a reverse logistics network for EOL assets. Second, we develop an MILP model in which the retrofitting option is integrated into the model to better analyze asset management strategies. This modeling approach is an improvement to previous asset management studies in that it incorporates (or includes) the retrofitting an option into asset management strategies. The goal here is to enable decision makers to accurately estimate operation and maintenance costs, the cost of purchasing an asset or retrofitting an existing asset, and optimal replacement timing. Last, reliability data is used to analyze the existing conditions of an asset, and based on the reliability/availability data, another model is developed and used to determine the asset management strategies for existing assets. In this study, we develop models that are applicable to any asset and illustrate these models on WTs over a finite horizon.
Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering