Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
Machine Learning
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Genetic Programming for Financial Time Series Prediction
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Botticelli: A Supply Chain Management Agent
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
MinneTAC Sales Strategies for Supply Chain TAC
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Price prediction and insurance for online auctions
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Redagent: winner of TAC SCM 2003
ACM SIGecom Exchanges
PackaTAC: a conservative trading agent
ACM SIGecom Exchanges
Controlling a supply chain agent using value-based decomposition
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Proceedings of the 8th annual conference on Genetic and evolutionary computation
CMieux: adaptive strategies for competitive supply chain trading
ACM SIGecom Exchanges
Pricing for customers with probabilistic valuations as a continuous knapsack problem
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Designing a successful trading agent for supply chain management
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (Intelligent Robotics and Autonomous Agents)
Adapting in agent-based markets: a study from TAC SCM
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A robust agent design for dynamic SCM environments
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Proceedings of the 11th International Conference on Electronic Commerce
Learning to predict the cost-per-click for your ad words
Proceedings of the 21st ACM international conference on Information and knowledge management
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Supply Chain Management (SCM) involves a number of interrelated activities from negotiating with suppliers to competing for customer orders and scheduling the manufacturing process and delivery of goods. Decision support systems for SCM need to be able to cope in uncertain, complex and highly competitive environments. Supporting dynamic strategies is a major but unresolved issue in the area. In this paper we examine two different approaches to address the issue of predicting customer offer prices that could result in orders in the domain of supply chain management. The first approach is to model the competitors' behaviour and predict their bidding prices according to the evolved models. The second one is to predict the lowest order prices for products for a number of days in the future using the time series of these prices. A set of algorithms are implemented based on Genetic Programming and Neural Networks learning techniques. The algorithms are tested in the TAC SCM simulated environment and the results are compared in terms of accuracy of prediction and execution time. Both learning techniques showed the potential for predicting prices in competitive and dynamic environments. The proposed Neural Networks algorithms demonstrate slightly better performance when tested in the TAC SCM environment compared to the algorithms implemented using Genetic Programming learning technique.