# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 8 Benefits of the Use of the Method  
In the sections above, a systematic method for capturing a virtual view of a maintenance depot's operations dealing with railway rolling stock is described. This method leads to the specification and population of a system-wide ontology, and a specification of relevant operations. This is illustrated with the acquisition of VMIs' data and shows how a short-term scheduling operation can be added to the system. Already towards the end of Section 5, we discussed the advantages of integrating the acquired knowledge graphs to inform the scheduling processes. In this section, the power of the integrated system is further explored, and in particular, we consider the improvement it makes compared to using stand-alone scheduling tools. The evaluation of the combined scheduling and resource retrieval process relies on constraint (2) of the MIP scheduling model as a key connecting point between the resource retrieval and scheduling processes (see Section 5 and 7, respectively). To set up the experiments, a real long-term schedule sample is being used. Scheme 7 illustrates a three-week schedule that involves several PM interventions, along with their due dates. For confidentiality reasons, the fleet identifier is converted to 111 in unit names and the PM interventions' names were hidden. The PM intervention names are converted to numbers and are shown in column 'MainIntervention', including 16 different maintenance interventions. These maintenance interventions are also grouped into groups as shown in column 'GR' following their depot fixed resources requirement. As no earliness/tardiness costs are available, see Section 7.3, the authors used the certification intervals to calculate the costs (see earliness cost column in Scheme 7). Assuming that the earliness cost of doing a maintenance intervention of the shortest interval is 10, all other costs are calculated referring to this. The tardiness cost will be higher than earliness costs due to government regulations and safety risks. The authors fixed the tardiness cost to 20 for all maintenance interventions, but this value can be derived from intervention criticality levels and penalties related to late maintenance. Scheme 8 depicts a short-term schedule produced by the MIP model, see Section 7.3, without considering the constraint (2), which requires inputs from the resource retrieval process, see Section 5. This shows four outputs for each date as the fleet availability constraint is set to '4', which means that for any day of the scheduling horizon, up to four units can be scheduled for maintenance. In this scenario, unit '11148' maintenance task '1', depicted in Scheme 7, is due on the 3 rd of May. The short-term schedule produced took the due date of maintenance intervention '1' required for unit '111481, as depot resources are available and depot capacity is sufficient (constraint 3). This intervention is scheduled just in time, as depicted in blue, where the Days Before Exam shows '0', meaning the scheduleddate is equals to the due date. When the maintenance is scheduled after the due date, this field value is negative. Likewise, maintenance '1' is scheduled for units '11145' and '11141' on the 10 th of May, which is the due date. However, before proceeding with the scheduling process, it is important to collect the available information about the necessary resources for every intervention within the schedule. The scheduling process can then include inputs about resource availability per maintenance intervention as the number of units that can be maintained, based on the store, before re-ordering. This option is covered by the MIP model discussed in Section 7.3 via constraint (2). Figure 11 shows the short-term scheduling process inputs from both ontology and resource retrieval process (described in Sections 7 and 4, respectively). When a resource retrieval process is used and thus constraint (2) is activated, two options are available.