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. ARTICLE HISTORY

Optimized Expansion Strategy for a Hydrogen Pipe Network in the Port of Rotterdam with Compound Real Options Analysis

“…Two-staged ROA applications have two options, generally to wait or invest and incorporate one or more uncertainty variables such as demand development or prices. In contrast, compound ROA applications have multiple options which progress in time [16,25,27,57]. Previous decisions lead to new options and compound ROAs optimize a sequence of decisions under one or more underlying uncertainty variables.…”

Optimization Model of Maintenance Scheduling Problem for Heritage Buildings with Constraint Programming

“…Many issues related to historical preservation have been documented, particularly regarding heritage buildings in urban areas. Several aspects are important, considering that the historical value of the building must be preserved [24,26]. Therefore, maintenance should be prioritized, as it not only safeguards the original structure's functionality but also enhances the socio-economic environment in its vicinity [27].…”

“…The maintenance system of old buildings, in particular, is often inadequate due to the decline in the building's condition [37]. Kalman and Létourneau [26] stated that the maintenance of heritage buildings is necessary to preserve their value. Munarim and Ghisi [32] conducted research related to heritage building maintenance to minimize significant negative impacts on the environment.…”

“…Based on the BCL, the model adjusts the appropriate type of maintenance required for each building, as shown in Table 2. According to Kalman et al [26], heritage building maintenance activities should be carried out as much as possible to minimize major repairs, which aim to maintain the value contained in the heritage building. Therefore, there are various options related to the maintenance type.…”

Constrained Multiagent Markov Decision Processes: a Taxonomy of Problems and Algorithms

“…Condition-based maintenance uses inspections and sensor diagnostics to decide about the maintenance to perform. Conditionbased maintenance emerges in several areas, including wind turbine maintenance (Byon & Ding, 2010), bridges and infrastructure (Neves & Frangopol, 2005;Van den Boomen et al, 2020) and aircraft components (Harman, 2002). From a decision-making point of view, the complexity can be found in deciding when and how maintenance is performed.…”