ABSTRACT

One of the key parts of a pavement management system is the maintenance and rehabilitation planning. The planning is usually developed under the assumption that all parameters are known with certainty. In practice, there are various parameters afflicted with large uncertainty. Ignoring the uncertainty may lead to a suboptimal plan adversely affecting the network conditions. The objective of this study is to develop an optimisation framework for network-level pavement maintenance and rehabilitation planning considering the uncertain nature of pavement deterioration and the budget with an applicable approach. A multistage stochastic mixed-integer programming model is proposed to find the optimal plan that is feasible for all possible scenarios of uncertainty and optimise the expectation of objective function. Two case studies of 4 and 21 pavement sections are presented to show the applicability of the proposed method. The value of stochastic solution and the expected value of perfect information which are the indices for evaluating the benefits of using the stochastic model are, respectively, 30% and 85% of the objective function of here and now model for the first case study and 26% and 42% of it regarding the second one. The indices are high indicating the effectiveness of the stochastic solution.

摘要

路面管理系统的关键部分之一是维护和修复计划。规划通常是在所有参数都已确定的假设下制定的。在实践中，各种参数都存在很大的不确定性。忽略不确定性可能会导致次优计划对网络状况产生不利影响。本研究的目的是开发一个网络级路面维护和修复规划的优化框架，考虑到路面恶化的不确定性和适用方法的预算。提出了一种多阶段随机混合整数规划模型，以找到对所有可能的不确定场景都可行的最优计划，并优化目标函数的期望。提出了 4 和 21 路段的两个案例研究，以显示所提出方法的适用性。随机解的值和完美信息的期望值是评估使用随机模型的好处的指标，分别是第一个案例研究和现在模型目标函数的 30% 和 85% 和 26 % 和 42% 与第二个有关。指数很高，表明随机解决方案的有效性。30% 和 85% 的此时此地模型的目标函数用于第一个案例研究，26% 和 42% 用于第二个案例研究。指数很高，表明随机解决方案的有效性。30% 和 85% 的此时此地模型的目标函数用于第一个案例研究，26% 和 42% 用于第二个案例研究。指数很高，表明随机解决方案的有效性。