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

Autor: Michel Aldanondo, Abdourahim Sylla, Thierry Coudert, Élise Vareilles, Laurent Geneste
Přispěvatelé: Gestion et Conduite des Systèmes de Production (G-SCOP_GCSP), Laboratoire des sciences pour la conception, l'optimisation et la production (G-SCOP), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Ecole Nationale d'Ingénieurs de Tarbes (ENIT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Centre Génie Industriel (CGI), IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole nationale supérieure des Mines d'Albi-Carmaux - IMT Mines Albi (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut polytechnique de Grenoble (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Université Grenoble Alpes - UGA (FRANCE)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
engineer-to-order
0209 industrial biotechnology
Operations research
Build to order
Process (engineering)
Computer science
Strategy and Management
0211 other engineering and technologies
02 engineering and technology
Management Science and Operations Research
Industrial and Manufacturing Engineering
[SPI]Engineering Sciences [physics]
020901 industrial engineering & automation
Autre
Selection (genetic algorithm)
Possibility theory
possibilistic pareto-dominance
021103 operations research
Bidding process
uncertainty and imprecision
Engineer-to-order
Uncertainty and imprecision
Pareto principle
technical bid selection
Bidding
Modélisation et simulation
Multiple-criteria decision analysis
multi-criteria decision making (MCDM)
Dominance (economics)
Multi-criteria decision making (MCDM)
Technical bid selection
Possibilistic pareto-dominance
Zdroj: International Journal of Production Research
International Journal of Production Research, Taylor & Francis, 2021, 59 (21), pp.6361-6381. ⟨10.1080/00207543.2020.1812754⟩
ISSN: 0020-7543
1366-588X
DOI: 10.1080/00207543.2020.1812754⟩
Popis: International audience; 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.
Databáze: OpenAIRE
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