For the optimal planning of maintenance schedules for infrastructural buildings (bridges, tunnels, etc) in urban road systems not only the budget has to be considered but also the impact on traffic to avoid unnecessary traffic jams. In a current research project we develop an optimization tool for this multiobjective problem based on ant colony optimization. In each iteration, the ants produce several different schedules for the maintenance over the next few years. Each of these schedules is formed by several scenarios of simultaneously closed roads. A parallel maintenance on different buildings can be modeled be introducing teams of ants. The scenarios are evaluated by an external traffic simulator. The quality of the different schedules, assessed by the waiting time created in the system, influences the amount of pheromone deposited on each schedule and therefore the probability that this or a similar schedule is chosen by the ants in the next iteration step. The building condition also has influence on the probability of choosing a certain schedule: Buildings in bad condition are getting more attractive to be chosen - thus avoiding that only buildings in good condition and therefore with low repair costs are scheduled for maintenance while buildings in bad condition are left to further deterioration. Additional constraints, e.g. budget constraints, can be introduced by applying a modification of the Elitist Ant strategy that guides the ants away from infeasible schedules.
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For the optimal planning of maintenance schedules for infrastructural buildings (bridges, tunnels, etc) in urban road systems not only the budget has to be considered but also the impact on traffic to avoid unnecessary traffic jams. In a current research project we develop an optimization tool for this multiobjective problem based on ant colony optimization. In each iteration, the ants produce several different schedules for the maintenance over the next few years. Each of these schedules is for...
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