Multiobjective Optimization of Ballasted Track Maintenance Scheduling

Abstract

This paper proposes a multiobjective optimization approach for a preventive maintenance scheduling problem. To model this problem, an optimization model was developed by considering two objective functions: minimizing maintenance cost associated with the preventive tamping cost, machine preparation cost, and possession cost during time scheduling, as well as minimizing the total loss of remaining useful life (LRUL) over a given planning horizon. The applicability of the model was tested with the strength Pareto evolutionary algorithm II (SPEA-II) and multiobjective particle swarm optimization (MOPSO) algorithms in a case study on a section of Tehran-Mashhad line in Iran for the planning horizon of three years. Finally, a sensitivity analysis was performed on the possession cost and maximum permitted train speed. The results showed that by increasing the maintenance budget up to 47.32%, the track capacity could be increased by 25% due to increased permitted speed. Moreover, by increasing the cost associated with track possession, the preparation cost was reduced by considering the opportunistic maintenance policy through the grouping track segments.