Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Authors:
Chang Ouk Kim;Ick-Hyun Kwon;Jun-Geol Baek
Affiliations:
Department of Information and Industrial Engineering, Yonsei University, Seoul, Republic of Korea 120-749;Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, USA 61801;Department of Business Administration, Kwangwoon University, Seoul, Republic of Korea 139-701
Venue:
Applied Intelligence
Year:
2008

Citing 0
Cited 5

Situation reactive approach to Vendor Managed Inventory problem

Expert Systems with Applications: An International Journal
An adaptive inventory control for a supply chain

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Multi-agent based distributed inventory control model

Expert Systems with Applications: An International Journal
Approximate dynamic programming for an inventory problem: Empirical comparison

Computers and Industrial Engineering
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models.