# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 7 Short-Term Scheduling
### Short-Term Scheduling Creation  
This section describes the process of short-term scheduling creation in terms of its inputs, outputs, and constraints related to depot resources and fleet availability.  
The main input, as shown in Figure 7 (process A2), is a previously produced and up-to-date long-term schedule specifying the PM interventions' due dates: an abstract example of this is given in Scheme 1 showing a list of maintenance tasks that are due for different units in the fleet. The long-term schedule is usually updated every day as a result of the daily update of the fleet units' mileage and maintenance status. As an example, the actual mileage of each unit on a given day will influence the short-term scheduling of the maintenance interventions based on the new due dates. Daily updates will inform the long-term scheduling process via mileage value update as shown in Figure 7 (processes A2 and A0).  
Figure 8: Interval-based maintenance.  
As depicted in Scheme 1, the long-term schedule specifies the next required maintenance task for each unit in the fleet and the due date for that task. The short-term scheduling process takes the due dates per unit where the maintenance intervention type is specified as an input.  
In addition to the constraint brought about by the long-term schedule, a set of constraints determine the short-term schedule (see Figure 9): (1) Depot resource constraints are also defined (such as Drops, Jacks, and Wheel Lathes). Constraints related to the depot resources should be considered such as the number of total and/or available maintenance roads, the working days (e.g., in this example only one road is used for exams and can receive two units per day), etc. (2) Other constraints related to the earliness/tardiness of the maintenance interventions such as the allowed tolerance in terms of early/late maintenance. (3) Fleet availability constraint (e.g., 5 units out of a fleet of 50 are allowed to be out of service per day). An example of a short-term schedule is given in Scheme 2. In this example, a constraint on the available roads for maintenance is considered where only two units might be received per day.  
When doing short-term scheduling, as depicted in Figure 9, either manually or using a computer-assisted technique (any technique automating the scheduling process), cost reduction is usually borne in mind, which may refer to various things such as the earliness/tardiness costs. For PM scheduling, reducing the gap between the due and the scheduled dates of the units' maintenance interventions is very important.  
Figure 9: Short-term scheduling problem abstraction.  
A long-term schedule (including due dates) is inferred using the units' mileage and maintenance intervention interval (see Figure 2). More than one long-term schedule can be considered when multiple sites and/or fleets are considered. In the following sections, two automated methods are introduced: the first one uses a computer-based method, without optimisation, mimicking the human manual scheduling process (see Section 7.2). The second one, discussed in Section 7.3, is a Mixed Integer Programming (MIP) model for short-term scheduling optimisation. A comparison between these two methods is given in Section 7.4.