Competitive bundling of categorized information goods
Proceedings of the 2nd ACM conference on Electronic commerce
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Winner determination in combinatorial auction generalizations
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
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Towards Automated Procurement via Agent-Aware Negotiation Support
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Electronic Commerce Research and Applications
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This paper presents iAuctionMaker as a novel tool that serves as a decision support for e-sourcing professionals on their pursuing of auction optimisation. Given a set of items to auction, iAuctionMaker helps an auctioneer determine how to separate items into promising bundles that are likely to produce better outcomes than the bundle of items as a whole. Promising bundles are those that satisfy certain properties believed to be present in competitive sourcing scenarios. These properties are defined by e-sourcing professionals and capture their experience and knowledge in the domain. iAuctionMaker models this knowledge as constraints to be satisfied by any bundle, and implements an optimisation algorithm to find the bundles that maximize satisfaction. Experimental results are shown to demonstrate the applicability of the approach. Case studies are presented to demonstrate that iAuctionMaker improves current e-sourcing practices and provides an alternative to combinatorial scenarios whose complexity hinders their application in actual-world sourcing scenarios.