Integrating discrete event simulation and genetic algorithm optimization for bridge maintenance planning.

To minimize agency and user costs in a bridge repair project, a bridge maintenance manager should develop an appropriate project schedule considering real-world constraints such as resource limitations (e.g., workspace and crew). This paper presents a new framework called Simulation-based Bridge Maintenance Optimization (SiBMO) by integrating Genetic Algorithm (GA) and Discrete Event Simulation (DES) to identify the optimum maintenance plan taking into account crew limitations. The framework optimizes the sequence of repair-activities in the repair interventions considering workspace limitations and predecessor relationships. SiBMO also develops a high-level schedule of the interventions regarding the project calendar and the Traffic Management Plan (TMP). The Bridge Information Model (BrIM) based user interface developed in this study visualizes the high-level schedule. The results of applying SiBMO on a real case study demonstrates its capability in finding the optimum maintenance plan, its efficiency in optimizing the high-level schedule, and its accuracy in estimating user costs. • The framework optimizes the bridge maintenance plan considering crew limitations. • It identifies the optimum schedule with the minimum user costs for repair projects. • It regards workspace and crew limitations to optimize the repair project schedule. • It finds a high-level project schedule regarding traffic plan and working hours. • This framework integrates the Genetic Algorithm and Discrete Event Simulation. [ABSTRACT FROM AUTHOR]