@article{chatzidimitriou09a,
  author =	 {Kyriakos C. Chatzidimitriou and Andreas
                  L. Symeonidis},
  title =	 {Data-Mining-Enhanced Agents in Dynamic
                  Supply-Chain-Management Environments},
  journal =	 {{IEEE} Intelligent Systems},
  volume =	 24,
  number =	 3,
  issn =	 {1541-1672},
  year =	 2009,
  pages =	 {54-63},
  doi =		 {10.1109/MIS.2009.51},
  publisher =	 {{IEEE} Computer Society},
  address =	 {Los Alamitos, CA, USA},
  abstract =	 {In modern supply chains, stakeholders with varying
                  degrees of autonomy and intelligence compete against
                  each other in a constant effort to establish
                  beneficiary contracts and maximize their own
                  revenue. In such competitive environments, entities
                  —software agents being a typical programming
                  paradigm—interact in a dynamic and versatile
                  manner, so each action can cause ripple reactions
                  and affect the overall result. In this article, the
                  authors argue that the utilization of data mining
                  primitives could prove beneficial in order to
                  analyze the supply-chain model and identify pivotal
                  factors. They elaborate on the benefits of data
                  mining analysis on a well-established agent
                  supply-chain management network, both at a macro and
                  micro level. They also analyze the results and
                  discuss specific design choices in the context of
                  agent performance improvement.},
  url =		 {http://jmvidal.cse.sc.edu/library/chatzidimitriou09a.pdf},
  cluster = 	 {12718310351742987781},
}