Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
An experimental analysis of multi-attribute auctions
Decision Support Systems
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AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
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Proceedings of the 4th ACM conference on Electronic commerce
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HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 7 - Volume 7
Combinatorial Auctions
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Expert Systems with Applications: An International Journal
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Computers and Industrial Engineering
Solving a sealed-bid reverse auction problem by multiple-criterion decision-making methods
Computers & Mathematics with Applications
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Expert Systems with Applications: An International Journal
An interval arithmetic based fuzzy TOPSIS model
Expert Systems with Applications: An International Journal
Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method
Expert Systems with Applications: An International Journal
Winner determination in discount auctions
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Mathematical and Computer Modelling: An International Journal
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Most of the existing reverse auction methods are restricted to the price dimension and assume that the qualitative criteria are fixed prior to competitive source selection. Multicriteria reverse auctions (MCRAs) facilitate the negotiation of price and other qualitative and quantitative criteria. In this paper, the winner determination (WD) problem of MCRAs is formulated as a multicriteria decision-making problem. An extension of the TOPSIS (technique for order preference by similarity to ideal solution) method based on fuzzy logic and interval arithmetic is proposed to solve the WD problem where some attributes are qualitative and imprecise in nature and others are quantitative but difficult to represent in precise numerical values. In some cases, precise values are inadequate to model the criteria in real life. Moreover, because of the personalities of human beings, the decision is based less on information and more on personal judgments, resulting in a biased decision. Therefore, fuzzy linguistic variables are used here to map qualitative criteria, and at the same time intervals, data are used for quantitative preferences that are difficult to represent in exact numerical values. The correlation coefficient and standard deviation integrated method is used for automatic enumeration of the criteria weights, and a mechanism is also developed to determine the preferences of qualitative criteria using fuzzy linguistic variables. We present illustrative example to demonstrate the applicability of the proposed method.