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Found 5 papers, 2 papers with code
Probability estimation and structured output prediction for learning preferences in last mile delivery
Results show that the zone transition probability estimation performs well, and that the structured output prediction learning can improve the results further.
Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP
These preferences are in the form of arc probabilities, i. e., the more preferred a route is, the higher is the joint probability.
Learn-n-Route: Learning implicit preferences for vehicle routing
Even in the case of changes in the customer sets, our method is able to find solutions that are closer to the actual routings than when using only distances, and hence, solutions that require fewer manual changes when transformed into practical routings.
Hybrid Classification and Reasoning for Image-based Constraint Solving
We explore the trade-off between the power of the classifier and the power of the constraint reasoning, as well as further integration through the additional use of structural knowledge.
Vehicle routing by learning from historical solutions
Even in the case of changes in the client sets, our method is able to find solutions that are closer to the actual route plans than when using distances, and hence, solutions that would require fewer manual changes to transform into the actual route plan.
