CMieux: adaptive strategies for competitive supply chain trading

Supply chains are a central element of today's global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The Supply Chain Trading Agent Competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading. TAC SCM pits against one another trading agents developed by teams from around the world. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon University's 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach to coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance.

[1]  D. Lambert,et al.  SUPPLY CHAIN MANAGEMENT , 2003 .

[2]  Norman M. Sadeh,et al.  The supply chain trading agent competition , 2005, Electron. Commer. Res. Appl..

[3]  Norman Sadeh,et al.  CMieux 2005: Design and Analysis of Carnegie Mellon University's entry in the Supply Chain Trading Agent Competition , 2006 .

[4]  Norman M. Sadeh,et al.  CMieux: adaptive strategies for competitive supply chain trading , 2006, SIGecom Exch..

[5]  Nicholas R. Jennings,et al.  Designing a successful trading agent for supply chain management , 2006, AAMAS '06.

[6]  N. Sadeh,et al.  What Differentiates a Winning Agent : An Information Gain Based Analysis of TAC-SCM , 2007 .

[7]  Peter Stone,et al.  Predictive Planning for Supply Chain Management , 2006, ICAPS.

[8]  Peter Stone,et al.  Agent-based supply chain management: bidding for customer orders , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[9]  John Collins,et al.  The Supply Chain Management Game for the 2007 Trading Agent Competition , 2004 .

[10]  Doina Precup,et al.  RedAgent-2003: an autonomous, market-based supply-chain management agent , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[11]  Peter Stone,et al.  Bidding for customer orders in TAC SCM , 2004, AAMAS'04.

[12]  Ari P. J. Vepsalainen.  Priority rules for job shops with weighted tardiness costs , 1987 .

[13]  Michael P. Wellman,et al.  Distributed Feedback Control for Decision Making on Supply Chains , 2004, ICAPS.

[14]  Norman M. Sadeh,et al.  Pricing for customers with probabilistic valuations as a continuous knapsack problem , 2006, ICEC '06.

[15]  Michael Carl Tschantz,et al.  Botticelli: a supply chain management agent , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[16]  David W. Hildum,et al.  MASCOT: An Agent-based Architecture for Coordinated Mixed-Initiative Supply Chain Planning and Scheduling , 1999 .

[17]  Michael P. Wellman,et al.  STRATEGIC INTERACTIONS IN A SUPPLY CHAIN GAME , 2005, Comput. Intell..