Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness

TL;DR

This paper presents a deep reinforcement learning approach for large-scale multi-sourcing inventory management, leveraging pre-trained deep learning models to simulate supply chain processes and improve decision-making under uncertainty.

Contribution

It introduces a scalable methodology combining deep RL with DL-based process simulation and a constraint coordination mechanism for supply chain optimization.

Findings

Enhanced performance on real-world datasets

Effective modeling of supply chain stochastic processes

Scalable approach for complex inventory management

Abstract

In this work, we study how to efficiently apply reinforcement learning (RL) for solving large-scale stochastic optimization problems by leveraging intervention models. The key of the proposed methodology is to better explore the solution space by simulating and composing the stochastic processes using pre-trained deep learning (DL) models. We demonstrate our approach on a challenging real-world application, the multi-sourcing multi-period inventory management problem in supply chain optimization. In particular, we employ deep RL models for learning and forecasting the stochastic supply chain processes under a range of assumptions. Moreover, we also introduce a constraint coordination mechanism, designed to forecast dual costs given the cross-products constraints in the inventory network. We highlight that instead of directly modeling the complex physical constraints into the RL optimization problem and solving the stochastic problem as a whole, our approach breaks down those supply chain processes into scalable and composable DL modules, leading to improved performance on large real-world datasets. We also outline open problems for future research to further investigate the efficacy of such models.