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Efficient interpretive ranking process incorporating implicit and transitive dominance relationships

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  • Sushil

    (Indian Institute of Technology Delhi)

Abstract

Interpretive ranking process (IRP) is a multi-criteria decision making method based on paired comparison in an interpretive manner. Due to paired comparisons, the number of interpretations to be made for n ranking variables are $$n(n-1)/2$$n(n-1)/2 to establish dominance with respect to each reference variable or criterion. IRP is a knowledge intensive method and thus a large number of comparisons poses a limitation on the number of rankling as well as reference variables to be considered in the design of the decision problem. This paper is intended to make the process of comparison more efficient so that this limitation on number of variables can be relaxed to handle comparatively large size problems as well. The number of interpretive comparisons can be drastically reduced by considering both implicit and transitive dominance relationships. It provides a critical review of IRP steps and suggests improvements to make it more efficient. It then illustrates the modified IRP method on a couple of already published examples (including an example on post-disaster management) and summarizes the reduction in interpretive comparisons that indirectly gives a measure of increase in its efficiency.

Suggested Citation

  • Sushil, 2019. "Efficient interpretive ranking process incorporating implicit and transitive dominance relationships," Annals of Operations Research, Springer, vol. 283(1), pages 1489-1516, December.
  • Handle: RePEc:spr:annopr:v:283:y:2019:i:1:d:10.1007_s10479-017-2608-y
    DOI: 10.1007/s10479-017-2608-y
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    References listed on IDEAS

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    1. Sushil, 2019. "Theory building using SAP-LAP linkages: an application in the context of disaster management," Annals of Operations Research, Springer, vol. 283(1), pages 811-836, December.
    2. Sushil, 2018. "How to check correctness of total interpretive structural models?," Annals of Operations Research, Springer, vol. 270(1), pages 473-487, November.
    3. Gwo-Hshiung Tzeng & Chi-Yo Huang, 2012. "Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems," Annals of Operations Research, Springer, vol. 197(1), pages 159-190, August.
    4. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    5. Jeng, Don Jyh-Fu & Huang, Kuo-Hsin, 2015. "Strategic project portfolio selection for national research institutes," Journal of Business Research, Elsevier, vol. 68(11), pages 2305-2311.
    6. Ho, William & Xu, Xiaowei & Dey, Prasanta K., 2010. "Multi-criteria decision making approaches for supplier evaluation and selection: A literature review," European Journal of Operational Research, Elsevier, vol. 202(1), pages 16-24, April.
    7. Vaidya, Omkarprasad S. & Kumar, Sushil, 2006. "Analytic hierarchy process: An overview of applications," European Journal of Operational Research, Elsevier, vol. 169(1), pages 1-29, February.
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    Cited by:

    1. Mohammad Rashed Hasan Polas & Valliappan Raju, 2021. "Technology and Entrepreneurial Marketing Decisions During COVID-19," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(2), pages 95-112, June.
    2. Parul Jain Gupta & Pradeep Kumar Suri, 2018. "Analysing the Influence of Improved Situation, Capability Level of Actors and Flexible Process Workflow on Public Value of E-Governance Projects in India," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(4), pages 349-372, December.
    3. Kyoo-Man Ha, 2017. "Three-Domain Model for Disaster Management Framework," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(4), pages 321-329, December.

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