Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Information distortion in a supply chain: the bullwhip effect
Management Science - Special issue on frontier research in manufacturing and logistics
Economic principles of multi-agent systems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Distributed rational decision making
Multiagent systems
Reducing the Bullwhip Effect by Means of Intelligent, Soft Computing Methods
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Designing And Managing The Supply Chain
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IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
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Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services
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AsiaMS '07 Proceedings of the IASTED Asian Conference on Modelling and Simulation
Wide-Area Traffic Simulation Based on Driving Behavior Model
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
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Decision Support Systems
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International Journal of Intelligent Information and Database Systems
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Decision Support Systems
A counterexample for the bullwhip effect: gaming and multiagent simulations
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
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The bullwhip effect is the amplification of the order variability in a supply chain. This phenomenon causes important financial cost due to higher inventory levels and agility reduction. In this paper, we study, for each company in a supply chain, the individual incentive to collaborate to reduce this problem. To achieve this, we simulate a supply chain inspired by the Québec forest industry, in which each company is an agent that uses one of three ordering schemes. Each ordering scheme represents a level of collaboration. One run of the simulation is done with fifty (50) weeks for each of the 3驴 = 729 combinations of these 3 ordering schemes among the 6 companies of the simulation. In each run, we evaluate each companyýs inventory holding and backorder costs. These outcomes are used to build a game in the normal form, which is next analyzed using Game Theory. In particular, we find two Nash equilibria incurring the minimum cost of the supply chain. We also note that there are no Nash equilibria in which some companies do not collaborate: collaborating companies have no incentive to stop collaboration.