Probability estimation and structured output prediction for learning preferences in last mile delivery. (arXiv:2201.10269v1 [cs.AI])

Web: http://arxiv.org/abs/2201.10269

We study the problem of learning the preferences of drivers and planners in
the context of last mile delivery. Given a data set containing historical
decisions and delivery locations, the goal is to capture the implicit
preferences of the decision-makers. We consider two ways to use the historical
data: one is through a probability estimation method that learns transition
probabilities between stops (or zones). This is a fast and accurate method,
recently studied in a VRP setting. Furthermore, we explore …

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