Vehicle Routing Optimization with unspecified capacity of different items

Question

I have a little different variant of vehicle route optimization problem. There are different items which are to be dropped at different stores. Each store require N items of amounts [a1, a2, ...., aN]. As we don't know how many stores will be there on a route beforehand. So how do we decide how much amount of a particular item to put in the vehicle? Or should I first just put random number of the different items in the vehicle and then use the capacity constraint. Please provide any link to research papers or blog which addresses this kind of problem.

Answer 1

No knowledge of how many stores will be on route beforehand: that's real-time planning or continuous planning (see OptaPlanner docs for both concepts): just replan when the vehicles are already driving.

As for predicting how many items to take along originally - that's predictive analytics. A poor man's implementation just fills the trucks up to their full capacity and optionally adds a soft constraint to spread the load across trucks and a soft constraint to avoid that the trucks all end up in the same region (so if new locations are added, it's likely there's a non-empty truck nearby). A rich man's implementations does probability and/or uncertainty calculations (think Bayes etc), but that's probably overkill.