Designing of an intelligent self-adaptive model for supply chain ordering management system

One of the challenging issues in supply chain management is the coordination of ordering processes, especially in dynamic situations. In recent years, reinforcement learning algorithms are considered to be efficient techniques for solving such problems. In this paper, an agent-based simulation techn...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2015-01, Vol.37, p.207-220
Main Authors: Mortazavi, Ahmad, Arshadi Khamseh, Alireza, Azimi, Parham
Format: Article
Language:English
Subjects:
Agent based simulation
Algorithms
Computer simulation
Learning
Markov decision process
Order disorder
Ordering policy
Reinforcement
Reinforcement learning
Sensitivity analysis
Simulation-based optimization
Statistical methods
Supply chain
Supply chains
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Summary:One of the challenging issues in supply chain management is the coordination of ordering processes, especially in dynamic situations. In recent years, reinforcement learning algorithms are considered to be efficient techniques for solving such problems. In this paper, an agent-based simulation technique has been integrated with a reinforcement learning algorithm and has been applied to model a four-echelon supply chain that faces non-stationary customer demands. This approach leads to the development of a novel and intelligent simulation-based optimization framework, which includes a detailed simulation modeling of supply chain behavior. Finally statistical methods, including the Var technique, are used for the risk evaluation and sensitivity analysis have been provided to support the decision making process under uncertainty.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2014.09.004