I have the following data:
- A fixed depot/facility location
- Dropping points with demands
- List of available vehicles (heterogeneous fleets)
What I am trying to do is, given the list of vehicles and dropping points, particular serving time and travelling time how should the vehicles be assigned?
Progress so far:
With the help of capacitated clustering, I have already clustered the locations. I have considered both the geospatial proximity and the demand of each point, such that the total radius of the cluster doesn't exceed a given radius and maximum demand doesn't exceed the lowest serving demand of the available vehicle (which in this case is 700 kg).
Each of these clusters has a particular serving time which I have computed, and there is a travelling time to the center of the cluster which I can compute with osrm package in
Each vehicle can be hired for a particular duration (E.g 12 hours)
Now, how to determine which clusters will be served by which vehicles given the time and capacity constraints? The cost associated is strictly a function of time, so if that can be optimized, then the total cost will be optimized too.
I am looking for some heuristics or a general approach to tackle the problem.