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 Vehicle availability

New to python and machine learning and had a look at various examples, titanic, iris classification, house price regression etc.

Looking if any examples similar to below which could look to adapt and best way to approach below.

Looking for machine learning to help solve the following problem.

Problem looking to solve.
If have many vehicles which not all fully loaded, would be better if we could identify which vehicles are nearby with enough capacity
to accomodate new loads which would help reduce number of vehicles on road at any time.

Any thoughts on how best to solve above problem using python.

Rough thoughts -
Two classes, vehicle and delivery_site. Vehicle has properties of location, capacity, and current_load. Delivery_site has a location and load. Loop through list of vehicles excluding those where capacity-current_load < load, and find the vehicle that is closest.

What you use to determine "closest", gets more interesting - it would be nice to use something like the Waze database so you can have up to the minute traffic info and can use time rather than crow-flies distance, but I have no idea how to approach that.
Thanks for suggestion.
It sounds like what you want to look into isn't really machine learning, but optimisation.
Thanks for link

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