Simultaneous optimization of rolling stock maintenance scheduling and rolling stock maintenance location choice

Abstract

The current research addresses a problem found in the area of railway operations regarding the maintenance of rolling stock units. It focuses on the situation in The Netherlands and approaches the problem from the perspective of its main railway operator N.V. Nederlandse Spoorwegen (NS).
The increasing use of the capacity of the railway network leads to two issues. First, the complexity of the scheduling process is increasing, raising the need for tools that automate this process. Second, since NS is considering to perform more maintenance activities during daytime, raising the question at which locations maintenance teams needs to be stationed to perform daytime maintenance. These issues are interrelated.
The model development in the current research, tackling the aforementioned issues, can be understood as a three-stage framework. Assuming a given rolling stock circulation, the first stage aims to find the maintenance schedule and maintenance location choice minimizing the total number of nighttime maintenance activities. The second stage introduces a model to compute the required capacity. The third stage integrates the first and second stage, aiming to find a solution to the first-stage model that satisfies some predetermined maintenance location capacity constraints that can be determined by the second-stage model.
First, it is shown that, for a scenario with 20 maintenance locations for daytime maintenance, up to 42.0% of the work can be performed during daytime. Also, the second model can be used to efficiently (i.e. within seconds) compute required capacity and an accurate maintenance activity planning. Moreover, results for the third model have been generated showing, for example, that in one considered problem instance, the number of maintenance shifts for which the required capacity exceeds the available capacity can be reduced from 21 to 5 in 7.6 minutes. In addition to the aforementioned experimental results, a more practical approach is taken as well by constructing a small use case, demonstrating how the current research can be applied in practical situations. To this end, planning software Viriato is used, by which various visualizations of maintenance schedules can be provided.