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A Robotic Mobile Fulfillment System is a robotised parts-to-picker systemthat is particularly well-suited for e-commerce warehousing. One distinguishingfeature of this type of warehouse is its high storage modularity. Numerousrobots are moving shelves simultaneously, and the shelves can be returned toany open location after the picking operation is completed. This work focuseson the real-time storage allocation problem to minimise the travel time of therobots. An efficient -- but computationally costly -- Monte Carlo Tree Searchmethod is used offline to generate high-quality experience. This experience canbe learned by a neural network with a proper coordinates-based featuresrepresentation. The obtained neural network is used as an action predictor inseveral new storage policies, either as-is or in rollout and supervised treesearch strategies. Resulting performance levels depend on the computing timeavailable at a decision step and are consistently better compared to real-timedecision rules from the literature.

Supervised learning and tree search for real-time storage allocation in Robotic Mobile Fulfillment Systems