Pavement Maintenance Program at the Network Level: Mixed-Integer Programming with Multiple Objectives

Abstract

Pavement network conditions deteriorate over the years of use. To keep pavement conditions at acceptable levels, highway agencies plan pavement maintenance and rehabilitation (M&R) programs and perform accordingly. Highway agencies usually face budget variability for pavement M&R activities because of limited resources, economic conditions, and changes in policies. The situation makes it difficult for highway agencies to keep an acceptable pavement condition at the network level. Therefore, it is important for highway agencies to adopt M&R policies that can maximize the network condition as well as handle the deviation of the network condition considering the available maintenance funds. In this paper, a multi-period multi-objective linear integer programming model is proposed. Two objectives, maximization of the average network condition and minimization of deviation of the network condition from an idealized network condition trend, are considered in the formulation. The model is formulated for fixed M&R budgets, as well as for variable M&R budgets. The proposed model provides an M&R program for the pavement network that helps decision makers to manage pavement maintenance programs considering budgetary constraints. A case study examining a network of 45 pavement sections is conducted. The solutions of the fixed-budget and variable-budget model are presented. In addition, the values of the system to the decision maker are discussed. Results show that the proposed model is an attractive way to manage pavement maintenance programs at the network level.

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

“…However, the above models consider fewer factors and are less reliable. So some scholars increase the consideration of influencing factors, not only considering the maintenance quality [12] and maintenance cost [13], but also considering resource consumption [14] and human toxicity (HT) [15], in an attempt to obtain more scientific program decision-making results from multiple dimensions.…”