Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains
This addresses inventory optimization for supply chain managers, but it is incremental as it adapts existing RL methods to a specific real-world scenario.
The paper tackles multi-product inventory management in supply chains by applying reinforcement learning to handle concurrent inventory for many products with shared capacity and stochastic demand, resulting in a method that maximizes sales and minimizes wastage for perishable products.
This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both adapted from a real-world business solution. The novelty of this problem with respect to supply chain literature is (i) we consider concurrent inventory management of a large number (50 to 1000) of products with shared capacity, (ii) we consider a multi-node supply chain consisting of a warehouse which supplies three stores, (iii) the warehouse, stores, and transportation from warehouse to stores have finite capacities, (iv) warehouse and store replenishment happen at different time scales and with realistic time lags, and (v) demand for products at the stores is stochastic. We describe a novel formulation in a multi-agent (hierarchical) reinforcement learning framework that can be used for parallelised decision-making, and use the advantage actor critic (A2C) algorithm with quantised action spaces to solve the problem. Experiments show that the proposed approach is able to handle a multi-objective reward comprised of maximising product sales and minimising wastage of perishable products.