Integrated Multi-stage Decision-Making for Winner Determination Problem in Online Multi-attribute Reverse Auctions Under Uncertainty

Abstract

Online multi-attribute reverse auctions (OMARA), which include many non-price attributes, aligns better to practice, and is prevalent in many fields such as project bidding and public sector procurement. In such auctions, the decision makers often face varying degrees of cognitive and environmental uncertainty. This renders the traditional winner (supplier) determination method based on deterministic values impracticable. Hence, from the standpoint of the auctioneer (purchaser), a new integrated decision framework under an uncertain situation is proposed. Firstly, the fuzzy set theory is applied to the winner determination problem in OMARA to recognize the uncertainty in the bidding attribute values. Secondly, the detail description of the winner determination problem in OMARA is provided. Thirdly, the comprehensive weights of the evaluation attributes are obtained by using fuzzy AHP and fuzzy deviation maximizing method together. Lastly, the five fuzzy multi-attribute decision-making methods are combined with simple dominant principle to evaluate the bidding alternatives and determine the winner (supplier). A numerical example is used to demonstrate the process of the proposed integrated decision frame-work, and the comparative analysis illustrates its feasibility and effectiveness.