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Using Ant Colony Optimization for Infrastructure Maintenance Scheduling
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lukas, Katharina Borrmann, André Rank, Erhard |
| Copyright Year | 2010 |
| Abstract | 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. The monetary objective is to steady the budget flow over the years. Costs can also be decreased by adjusting the maintenance schedule with the maintenance work of third parties also using the building such as public services and public transport organizations. Additional constraints exist which are known to the planner but are too complicated to be modeled in the planning software. So the planning tool should give as result a set of solutions that satisfy the given constraints for the planner to choose from. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cms.bgu.tum.de/publications/paper_Lukas_ECPPM2010.pdf |
| Alternate Webpage(s) | http://www.andre-borrmann.de/docs/paper_Lukas_ECPPM2010.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Adaptation Algorithm Ant colony optimization algorithms Ants Apache Ant (Another Neat Tool) Choice Behavior Choose (action) Constraint (mathematics) Iteration Loss function Mathematical optimization Money Multi-objective optimization Optimization problem Pheromone Program optimization Schedule (computer science) Schedule (document type) Scheduling (computing) Scheduling - HL7 Publishing Domain Simulation Simulators Solutions Vendor Information Documentation teams |
| Content Type | Text |
| Resource Type | Article |
