A railway system needs a substantial amount of maintenance. To prevent unexpected breakdowns as much as possible, preventive maintenance is required. In this paper we discuss the Preventive Maintenance Scheduling Problem (PMSP), where (short) routine activities and (long) unique projects have to be scheduled in a certain period. To reduce costs and inconvenience for the travellers and operators, these activities have to be scheduled as much as possible together. We present a mathematical formulation for this problem and some greedy heuristics to solve it fast. Moreover, we compare the performance of these heuristics with the optimal solution using some randomly generated instances.
A railway system needs a substantial amount of maintenance. To prevent unexpected breakdowns as much as possible, preventive maintenance is required. In this paper we discuss the Preventive Maintenance Scheduling Problem (PMSP), where (short) routine activities and (long) unique projects have to be scheduled in a certain period. To reduce costs and inconvenience for the travellers and operators, these activities have to be scheduled as much as possible together. We present a mathematical formulation for this problem and some greedy heuristics to solve it fast. Moreover, we compare the performance of these heuristics with the optimal solution using some randomly generated instances.
This paper addresses the Rolling Stock Rebalancing Problem (RSRP) which arises within a passenger railway operator when the rolling stock has to be rescheduled due to changing circumstances. RSRP is relevant both in the short-term planning stage and in the real-time operations.RSRP has as input a timetable and a rolling stock circulation where the allocation of the rolling stock among the stations at the start or at the end of a certain planning period does not match with the allocation before or after that planning period. The problem is then to modify the input rolling stock circulation in such a way that the number of remaining off-balances is minimal. If all off-balances have For practical usage of solution approaches for RSRP, it is important to solve the problem quickly. Since we prove that RSRP is NP-hard, we focus on heuristic solution approaches: we describe two heuristics and compare them with each other on (variants of) real-life instances of NS, the main Dutch passenger railway operator. Finally, to get further insight in the quality of the proposed heuristics, we also compare their outcomes with optimal solutions obtained by solving an existing rolling stock circulation model.
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