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

作者：Mortazavi Ahmad; Khamseh Alireza Arshadi^*; Azimi Parham

来源：Engineering Applications of Artificial Intelligence, 2015, 37: 207-220.

DOI：10.1016/j.engappai.2014.09.004

• Supply chain
• Ordering policy
• Agent based simulation
• Reinforcement learning
• Markov decision process
• Simulation-based optimization

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.

• 出版日期2015-1