A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems

TL;DR

This paper introduces a deep Q-network based reinforcement learning algorithm to optimize inventory decisions in the beer game, demonstrating near-optimal performance and adaptability in decentralized supply chain scenarios.

Contribution

The paper presents a novel deep reinforcement learning approach for inventory optimization in the beer game, capable of handling irrational agent behaviors and adaptable through transfer learning.

Findings

Outperforms base-stock policy with realistic human behavior.

Achieves near-optimal order quantities in cooperative supply chain settings.

Supports transfer learning for different agents and cost scenarios.

Abstract

The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate the bullwhip effect. The game is a decentralized, multi-agent, cooperative problem that can be modeled as a serial supply chain network in which agents cooperatively attempt to minimize the total cost of the network even though each agent can only observe its own local information. Each agent chooses order quantities to replenish its stock. Under some conditions, a base-stock replenishment policy is known to be optimal. However, in a decentralized supply chain in which some agents (stages) may act irrationally (as they do in the beer game), there is no known optimal policy for an agent wishing to act optimally. We propose a machine learning algorithm, based on deep Q-networks, to optimize the replenishment decisions at a given stage. When playing alongside agents who follow a base-stock policy, our algorithm obtains near-optimal order quantities. It performs much better than a base-stock policy when the other agents use a more realistic model of human ordering behavior. Unlike most other algorithms in the literature, our algorithm does not have any limits on the beer game parameter values. Like any deep learning algorithm, training the algorithm can be computationally intensive, but this can be performed ahead of time; the algorithm executes in real time when the game is played. Moreover, we propose a transfer learning approach so that the training performed for one agent and one set of cost coefficients can be adapted quickly for other agents and costs. Our algorithm can be extended to other decentralized multi-agent cooperative games with partially observed information, which is a common type of situation in real-world supply chain problems.