Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks
Data Mining-Driven Analysis and Decomposition in Agent Supply Chain
Management Networks
Kyriakos C. Chatzidimitriou1, Andreas L. Symeonidis1,2 and Pericles A. Mitkas1,2
1
Department of Electrical and Computer Engineering
Aristotle University of Thessaloniki, GR541 24, Thessaloniki, Greece
2
Intelligent Systems and Software Engineering Laboratory
Informatics and Telematics Institute/CERTH, 57001, Thessaloniki, Greece
{kyrcha,asymeon}@issel.ee.auth.gr, mitkas@auth.gr
Abstract
In complex and dynamic environments where interdependencies cannot monotonously determine causality, data mining techniques may be employed in order to analyze the problem, extract key features and identify pivotal factors.
Typical cases of such complexity and dynamicity are supply chain networks, where a number of involved stakeholders struggle towards their own benefit. These stakeholders may be agents with varying degrees of autonomy and intelligence, in a constant effort to establish beneficiary contracts and maximize own revenue. In this paper, we illustrate the benefits of data mining analysis on a well-established agent supply chain management network. We apply data mining techniques, both at a macro and micro level, analyze the results and discuss them in the context of agent performance improvement. 1. Introduction
As agent technology matures in time, autonomous agents gain applicability and trust in trading and auctioning goods in real-world electronic markets, as well as in managing more complex environments like supply chain networks [4, 8]. Various approaches are followed in order to determine the optimal agent strategy with respect to the challenges they (agents) come up against. The plethora of data generated by these highly dynamic markets can be exploited in various contexts, either for online, or for a posteriori analysis. Our work attempts to evaluate data mining (DM) methodologies for analyzing and improving agent behavior based on market data
Management Networks
Kyriakos C. Chatzidimitriou1, Andreas L. Symeonidis1,2 and Pericles A. Mitkas1,2
1
Department of Electrical and Computer Engineering
Aristotle University of Thessaloniki, GR541 24, Thessaloniki, Greece
2
Intelligent Systems and Software Engineering Laboratory
Informatics and Telematics Institute/CERTH, 57001, Thessaloniki, Greece
{kyrcha,asymeon}@issel.ee.auth.gr, mitkas@auth.gr
Abstract
In complex and dynamic environments where interdependencies cannot monotonously determine causality, data mining techniques may be employed in order to analyze the problem, extract key features and identify pivotal factors.
Typical cases of such complexity and dynamicity are supply chain networks, where a number of involved stakeholders struggle towards their own benefit. These stakeholders may be agents with varying degrees of autonomy and intelligence, in a constant effort to establish beneficiary contracts and maximize own revenue. In this paper, we illustrate the benefits of data mining analysis on a well-established agent supply chain management network. We apply data mining techniques, both at a macro and micro level, analyze the results and discuss them in the context of agent performance improvement. 1. Introduction
As agent technology matures in time, autonomous agents gain applicability and trust in trading and auctioning goods in real-world electronic markets, as well as in managing more complex environments like supply chain networks [4, 8]. Various approaches are followed in order to determine the optimal agent strategy with respect to the challenges they (agents) come up against. The plethora of data generated by these highly dynamic markets can be exploited in various contexts, either for online, or for a posteriori analysis. Our work attempts to evaluate data mining (DM) methodologies for analyzing and improving agent behavior based on market data
References: [1] R. Arunachalam and N. M. Sadeh. The Supply Chain Trading Agent Competition. Electronic Commerce Research and Applications, 4:6684, 2005. with Uncertainty and Variation in SCM Environments. Expert Systems with Applications, Article in Press, 2007. Forecasting Market Prices in a Supply Chain Game. In Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, pages 1318–1325, Honolulu, Hawaii, 2007. Trading Agents Design and Analysis, 2004. Computing. Elsevier, 2007.