Reinforcement Learning for Efficient Returns Management
This addresses inefficiency and cost reduction in retail logistics, though it is an incremental application of existing methods to a specific domain.
The paper tackled the problem of inefficient returns management in retail warehouses by proposing a reinforcement learning approach to instantly reallocate returned products to stores, reducing average storage time by 96% with only a 3% performance gap compared to offline methods.
In retail warehouses, returned products are typically placed in an intermediate storage until a decision regarding further shipment to stores is made. The longer products are held in storage, the higher the inefficiency and costs of the returns management process, since enough storage area has to be provided and maintained while the products are not placed for sale. To reduce the average product storage time, we consider an alternative solution where reallocation decisions for products can be made instantly upon their arrival in the warehouse allowing only a limited number of products to still be stored simultaneously. We transfer the problem to an online multiple knapsack problem and propose a novel reinforcement learning approach to pack the items (products) into the knapsacks (stores) such that the overall value (expected revenue) is maximized. Empirical evaluations on simulated data demonstrate that, compared to the usual offline decision procedure, our approach comes with a performance gap of only 3% while significantly reducing the average storage time of a product by 96%.