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.

Automated summary

The study presents a new framework called SiBMO to optimize bridge maintenance, integrating Genetic Algorithm and Discrete Event Simulation to identify the best maintenance plan while considering crew limitations. By optimizing the sequence of repair activities and high-level schedule, SiBMO improves efficiency and accuracy in project management.