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An Adaptive ANP & ELECTRE IS-Based MCDM Model Using Quantitative Variables

Author

Listed:
  • Antonio J. Sánchez-Garrido

    (Department of Construction Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Ignacio J. Navarro

    (Department of Construction Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

  • José García

    (School of Construction and Transportation Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

  • Víctor Yepes

    (Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 Valencia, Spain)

Abstract

The analytic network process (ANP) is a discrete multi-criteria decision-making (MCDM) method conceived as a generalization of the traditional analytic hierarchical process (AHP) to address its limitations. ANP allows the incorporation of interdependence and feedback relationships between the criteria and alternatives that make up the system. This implies much more complexity and intervention time, which reduces the expert’s ability to make accurate and consistent judgments. The present paper takes advantage of the usefulness of this methodology by formulating the model for exclusively quantitative variables, simplifying the decision problem by resulting in fewer paired comparisons. Seven sustainability-related criteria are used to determine, among four design alternatives for a building structure, which is the most sustainable over its life cycle. The results reveal that the number of questions required by the conventional AHP is reduced by 92%. The weights obtained between the AHP and ANP groups show significant variations of up to 71% in the relative standard deviation of some criteria. This sensitivity to subjectivity has been implemented by combining the ANP-ELECTRE IS methods, allowing the expert to reflect the view of the decision problem with greater flexibility and accuracy. The sensitivity of the results on different methods has been analyzed.

Suggested Citation

  • Antonio J. Sánchez-Garrido & Ignacio J. Navarro & José García & Víctor Yepes, 2022. "An Adaptive ANP & ELECTRE IS-Based MCDM Model Using Quantitative Variables," Mathematics, MDPI, vol. 10(12), pages 1-24, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2009-:d:836142
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    References listed on IDEAS

    as
    1. Thomas L. Saaty, 1986. "Axiomatic Foundation of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 32(7), pages 841-855, July.
    2. Shapiro, Arnold F. & Koissi, Marie-Claire, 2017. "Fuzzy logic modifications of the Analytic Hierarchy Process," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 189-202.
    3. Jicang Xu & Linlin Li & Ming Ren, 2022. "A Hybrid ANP Method for Evaluation of Government Data Sustainability," Sustainability, MDPI, vol. 14(2), pages 1-32, January.
    4. Sébastien Bigaret & Richard E. Hodgett & Patrick Meyer & Tatiana Mironova & Alexandru-Liviu Olteanu, 2017. "Supporting the multi-criteria decision aiding process: R and the MCDA package," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 169-194, November.
    5. Vicent Penadés-Plà & Tatiana García-Segura & José V. Martí & Víctor Yepes, 2016. "A Review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design," Sustainability, MDPI, vol. 8(12), pages 1-21, December.
    6. Ignacio J. Navarro & José V. Martí & Víctor Yepes, 2021. "Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices," Mathematics, MDPI, vol. 9(5), pages 1-19, February.
    7. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    8. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    9. Alexis Tsoukiàs, 2007. "On the concept of decision aiding process: an operational perspective," Annals of Operations Research, Springer, vol. 154(1), pages 3-27, October.
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