Dynamic Programming of 0/1 Knapsack Problem for Network-Level Pavement Asset Management System

For Low Volume Roads (LVRs), selecting maintenance and rehabilitation (M) activities have become a major concern for most transportation agencies. At large-scale road networks, the combinatorial explosion of the feasible solution is a major difficulty with treatment activities programming. As part of the Wyoming Technology Transfer Center (WYT2/LTAP) efforts to develop a comprehensive optimization analysis in Wyoming, this research study developed a user-friendly optimization algorithm, using a dynamic programming implementation of the 0/1 Knapsack problem. The developed algorithm utilized the Ant Colony Optimization (ACO) algorithm, which is one of the Swarm Intelligence (SI) optimization sub-packages, to provide locally-optimal solutions (sub-sets) to select the optimal treatments for road networks at a large-scale. Ultimately, a case study of 318 county roads in Wyoming is analyzed, and the results of different scenarios and current network performance are compared. The developed optimization algorithm helps the decision-makers in selecting the optimum set of roads that can maximize the overall pavement performance within a limited maintenance budget.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author