Communication design for electronic negotiations on the basis of XML schema
Proceedings of the 10th international conference on World Wide Web
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Cooperative vs. Competitive Multi-Agent Negotiations in Retail Electronic Commerce
CIA '98 Proceedings of the Second International Workshop on Cooperative Information Agents II, Learning, Mobility and Electronic Commerce for Information Discovery on the Internet
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An electronic market platform usually requires buyers and sellers to exchange offers-to-buy and offers-to-sell. The goal of this exchange is to reach an agreement on the suitability of closing transactions between buyers and sellers. In this paper we use multilateral and integrative e-negotiations to investigate our approach which attempts to find the best buyer-seller pairs, for an equal number of buyers and seller, using either matchmaking or a well-tested genetic algorithm: NSGA-II. The goal is to match as many buyers and sellers as closely as possible on five objectives (i.e., quality, quantity, price, delivery and payment) that vary randomly between a given range for buyers and are fixed for sellers. Experiments are performed and results are discussed for both approaches. The main finding is that there is a trade-off between solution quality and execution time: The genetic algorithm is capable of finding higher quality solutions than matchmaking when a suitable population size is employed, but matchmaking's execution time is significantly faster. This allows in turn to predict which technique to use depending on quality and speed in an e-negotiation scenario.