MinneTAC Sales Strategies for Supply Chain TAC

Authors:
Wolfgang Ketter;Elena Kryzhnyaya;Steven Damer;Colin McMillen;Amrudin Agovic;John Collins;Maria Gini
Affiliations:
University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota
Venue:
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Year:
2004

Citing 5
Cited 6

TAC-03: a supply-chain trading competition

AI Magazine
Redagent: winner of TAC SCM 2003

ACM SIGecom Exchanges
TacTex-03: a supply chain management agent

ACM SIGecom Exchanges
Botticelli: a supply chain management agent designed to optimize under uncertainty

ACM SIGecom Exchanges
PackaTAC: a conservative trading agent

ACM SIGecom Exchanges

Adaptive strategies for predicting bidding prices in supply chain management

Proceedings of the 10th international conference on Electronic commerce
Dynamic regime identification and prediction based on observed behavior in electronic marketplaces

AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Detecting and forecasting economic regimes in multi-agent automated exchanges

Decision Support Systems
Learning approaches for developing successful seller strategies in dynamic supply chain management

Information Sciences: an International Journal
Identifying and forecasting economic regimes in TAC SCM

AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes

Information Systems Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe two sales strategies used by our agent, MinneTAC, for the 2003 Supply Chain Management Trading Agent Competition (TAC SCM). Both strategies estimate, as the game progresses, the probability of receiving a customer order for different prices and compute the expected profit. We empirically analyze the effect of the discount given by suppliers on orders made the first day of the game, and show that in high-demand games there is a strong correlation between the performance of an agent in the game and the offers it receives from suppliers the first day of the game.