Possibilistic Pareto-dominance approach to support technical bid selection under imprecision and uncertainty in engineer-to-order bidding process - Archive ouverte HAL Access content directly
Journal Articles International Journal of Production Research Year : 2021

Possibilistic Pareto-dominance approach to support technical bid selection under imprecision and uncertainty in engineer-to-order bidding process

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Abstract

Successful bidding involves defining relevant technical bid solutions that conform to the customers' requirements, then selecting the most interesting one for the commercial offer. However, in Engineer-To-Order (ETO) industrial contexts, this selection process is complicated by issues of imprecision, uncertainty and confidence regarding the values of the decision criteria. To address this complexity, a Multi-Criteria Decision Making (MCDM) support approach is proposed in this study. This approach is based on possibility theory and the Pareto-dominance principle. It involves three main stages. First, a method is proposed to automatically model the values of the decision criteria by possibility distributions. Second, four possibilistic mono-criterion dominance relations are developed to compare two solutions with respect to a single decision criterion. Finally, an interactive method is devised to determine the most interesting technical bid solutions with respect to all the decision criteria. The method is applied to the design of a technical bid solution of a crane. The results show that this approach enables bidders to select the most interesting solution during a bidding process, while taking into account imprecision, uncertainty and their own confidence regarding the values of the decision criteria.
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Dates and versions

hal-02946741 , version 1 (23-09-2020)

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Abdourahim Sylla, Thierry Coudert, Élise Vareilles, Laurent Geneste, Michel Aldanondo. Possibilistic Pareto-dominance approach to support technical bid selection under imprecision and uncertainty in engineer-to-order bidding process. International Journal of Production Research, 2021, 59 (21), pp.6361-6381. ⟨10.1080/00207543.2020.1812754⟩. ⟨hal-02946741⟩
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