A generic DirectBackprop deep RL policy, trained only on historical demand across many products, matches or beats classical inventory heuristics in five problem settings, and structural monotonicity penalties improve out-of-sample robustness.
Effective Dual-Sourcing Through Inventory Projection
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abstract
We consider a single-echelon inventory system under periodic review with two suppliers facing stochastic demand, where excess demand is backlogged. The expedited supplier has a shorter lead time than the regular supplier but charges a higher unit price. We introduce the Projected Expedited Inventory Position (PEIP) policy, and we show that the relative difference between the long run average cost per period of this policy and the optimal policy converges to zero when both the shortage cost and the cost premium for expedited units become large, with their ratio held constant. A corollary of this result is that several existing heuristics are also asymptotically optimal in this non-trivial regime. We show through an extensive numerical investigation that the PEIP policy outperforms the current best performing heuristic policies in literature.
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Structure-Informed Deep Reinforcement Learning for Inventory Management
A generic DirectBackprop deep RL policy, trained only on historical demand across many products, matches or beats classical inventory heuristics in five problem settings, and structural monotonicity penalties improve out-of-sample robustness.