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Found 3 papers, 3 papers with code
Learning Dynamic Selection and Pricing of Out-of-Home Deliveries
We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices.
Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces
Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning.
Distance approximation to support customer selection in vehicle routing problems
Estimating the solution value of transportation problems can be useful to assign customers to days for multi-period vehicle routing problems, or to make customer selection decisions very fast (e. g., within an online environment).
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