Amsterdam's extensive network of bridges and quay walls is facing a critical maintenance backlog due to structural underfunded infrastructure maintenance. The many upcoming bridge maintenance projects to ensure safety are expected to cause significant more hindrance to road users. However, complex road networks with high traffic density in urban areas like Amsterdam make it difficult to foresee how the effects of different maintenance projects interact and how the projects can be planned efficiently to reduce hindrance.
This study aims to optimise the planning of a given set of bridge maintenance projects to reduce hindrance to road users, combining a genetic algorithm with traffic simulations using a new estimation method to improve the assessment of simultaneous closures. The genetic algorithm selects the optimal starting time and execution duration for each bridge while minimising maintenance cost and additional travel times for the modalities car, freight and bicycle. Using the fast static traffic simulation of the Urban Strategy Tool of TNO, a new estimation method for evaluating simultaneous closures is proposed, accounting for interdependencies between bridges. Results of a case study on four urban bridges in Amsterdam show that the new method provides more accurate estimates of additional travel time compared to previous methods and generates better solutions. Moreover, the fast proposed optimisation framework makes it possible to evaluate multiple scenarios in reasonable time to assist in decision making of maintenance planning.