APPLICATION OF MARKOV DECISION PROCESS TO LEVEL-OF-SERVICE-BASED MAINTENANCE SYSTEMS
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Systems based on level of service (LOS) currently implemented to manage highway maintenance use extensive subjective data. Collection of these data is tedious and expensive and the inherent uncertainties in the data render the results imprecise. A modified method using the Markov decision process (MDP), which overcomes most of the drawbacks of the LOS-based systems, is described. The adoption of the MDP is consistent with progressive evolution in the field of highway maintenance management. It introduces measures of performance benefits that are less subjective than those used in the NCHRP LOS model, which rely on attributes and utility functions. The modified model uses three types of key input data: transition probabilities, costs, and relative-importance weights. The transition probabilities are computed analytically using sample deterioration models and quality standards. An approach is described for computing the cost of each alternative from historical data. An analytic approach is also described for computing relative-importance weights using simple ranking of and comparison scores for the highway elements. This method was tested with 58 highway elements in 12 strata and 3 levels of service each. The resulting problem, which had 2,088 variables and 697 constraints, required less than 15 min on an IBM PC using an off-the-shelf linear programming package. The results of the test were consistent with the input data and demonstrated that the objectives set for the method were being met. Although the method was tested with mostly roadside elements, it can generally be used with any LOS-based system.