An ant colony algorithm based on opportunities for scheduling the preventive railway maintenance
Résumé
Railway infrastructure maintenance is of fundamental importance in order to ensure a good service in terms of punctuality, safety and efficiently operation of trains on railway track and also for passenger comfort. Track maintenance covers a large amount of different activities such as inspections, repairs, replacement of failed components or modules and renewals. In this paper, we address the problem of scheduling the preventive railway maintenance activities. The goal is to prevent track failure probability and breakdowns to guarantee a stable and safe service in specified conditions. These activities ensure the increasing of the system reliability and its availability but require considerable resources and large costs, which can be minimized by scheduling the maintenance operations. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. Thus, we propose an ant colony optimization (ACO) method based on opportunities to deal with this problem. The performance of our proposed ACO algorithm is tested by numerical experiments on a large number of randomly generated instances. A comparison with optimal solutions are presented. The results show the effectiveness of our proposed method.