Linear programming 1: introduction
Linear programming 1: introduction
A stochastic programming approach to scheduling in TAC SCM
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Botticelli: A Supply Chain Management Agent
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
RedAgent-2003: An Autonomous Market-Based Supply-Chain Management Agent
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Redagent: winner of TAC SCM 2003
ACM SIGecom Exchanges
Value-driven procurement in the TAC supply chain game
ACM SIGecom Exchanges
TacTex-03: a supply chain management agent
ACM SIGecom Exchanges
The supply chain trading agent competition
Electronic Commerce Research and Applications
Empirical game-theoretic analysis of the TAC Supply Chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Forecasting market prices in a supply chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Adaptive strategies for predicting bidding prices in supply chain management
Proceedings of the 10th international conference on Electronic commerce
Forecasting market prices in a supply chain game
Electronic Commerce Research and Applications
Flexible decision control in an autonomous trading agent
Electronic Commerce Research and Applications
Fuzzy adaptive agent for supply chain management
Web Intelligence and Agent Systems
Thesis summary: empirical game-theoretic methods for strategy design and analysis in complex games
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Agent-assisted supply chain management: Analysis and lessons learned
Decision Support Systems
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We present and evaluate the design of Deep Maize, our entry in the 2005 Trading Agent Competition Supply Chain Management scenario. The central idea is to decompose the problem by estimating the value of key resources in the game. We first create a high-level production schedule that considers cross-cutting constraints and future decisions, but abstracts aways from the details of sales and purchasing. We then make specific sales and purchasing decisions separately, coordinating these decisions with the high-level schedule using resource values derived from the schedule. All of these decisions are made using approximate optimization techniques and make use of explicit predictions about market conditions. Deep Maize was one of the most successful agents in the 2005 tournament, both in overall performance and on specific measures that emphasize coordination.