I have the following problem. There is a service station that can provide service for a number of vehicles at the same time. The service data looks like this:
Vehicle ServicePlanStart ServicePlanEnd ServiceTrueStart ServiceTrueEnd 0 A 2022-08-01 08:00:00 2022-08-01 13:00:00 2022-08-01 09:00:00 2022-08-01 14:00:00 1 B 2022-08-01 10:00:00 2022-08-01 15:00:00 2022-08-01 09:00:00 2022-08-01 14:00:00 2 C 2022-08-01 11:00:00 2022-08-01 13:00:00 2022-08-01 11:00:00 2022-08-01 13:00:00 3 D 2022-08-01 12:00:00 2022-08-01 17:00:00 2022-08-01 14:00:00 2022-08-01 18:00:00
We have plan times and true times that are often different, as not all vehicles come in time (too late/too early) for service, or some need to wait for a free slot (because others came too late, etc.). There may be a different number of vehicles at the service station simultaneously. My task is to predict the actual start and end times ('ServiceTrueStart' and 'ServiceTrueEnd') or the status (in service/not in service) of a vehicle at some time point in the future (in the next x hours). For example, let's say we have 9:30 now and want to predict true start/end values (or status in x hours) for all 4 vehicles: As we can see, vehicles A and B are already in service from 9:00 (so we already know their 'ServiceTrueStart' times). How can we predict the rest of the true values (or status in x hours) that we don't know at that time point? What kind of algorithm should I use? How can I transform my data, so it's possible to use classic machine/deep learning algorithms? Any ideas are highly appreciated. This example is simplified and in reality I have about 2000 vehicles and a few hundred service stations. The business problem I try to solve here is how many vehicles I have available at some time point in the future.