Optimal Infrastructure Maintenance Scheduling Problem Under Budget Uncertainty
Lu Gao, Zhanmin Zhang, University of Texas at Austin, May 2011, 68 pp. (161028-1)
This research addresses a general class of infrastructure asset management problems. Infrastructure agencies usually face budget uncertainties that will eventually lead to suboptimal planning if maintenance decisions are made without taking the uncertainty into consideration. It is important for decision makers to adopt maintenance scheduling policies that take future budget uncertainty into consideration. The author proposes a multistage, stochastic linear programming model to address this problem. The author also develops solution procedures using the augmented Lagrangian decomposition algorithm and scenario reduction method. A case study exploring the computational characteristics of the proposed methods is conducted and the benefit of using the stochastic programming approach is discussed.
Keywords: Stochastic Linear Programming, Lagrangian Algorithm, Infrastructure Asset Management, IAM
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