Infrastructure maintenance and replacement optimization under multiple uncertainties and managerial flexibility

Abstract

Infrastructure maintenance and replacement decisions are subject to uncertainties such as regular asset degradation, structural failure, and price uncertainty. In the engineering domain, Markov Decision Processes (MDPs) typically focus on uncertainties regarding asset degradation and structural failure. While the literature in the engineering domain stresses the importance of addressing price uncertainties, it does not substantiate the observations of such uncertainties through optimization modeling. By contrast, real option analyses (ROAs) that originate from the financial domain address price uncertainties but generally disregard asset degradation and structural failure. Accordingly, this piece of current research brings both domains closer together and proposes an optimization approach that incorporates the flexibility to choose between multiple successive intervention strategies, regular asset degradation, structural failure and multiple price uncertainties. A practical result of the current research is a realistic approach to optimization modeling in which state space reduction is achieved by combining prices into portfolios. The current research obtains transition probabilities from existing price data. This approach is demonstrated using a case study of a water authority in the Netherlands and confirms the premise that price fluctuations may influence short-term maintenance and replacement decisions.