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Enhanced fuzzy-analytic hierarchy process

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Abstract

Application of fuzzy-analytic hierarchical process (fuzzy-AHP) has been growing continuously to select the best alternative. In the fuzzy-AHP approach, first the complex problem is itemized into a hierarchical structure for pairwise comparisons. Once a comparison matrix is formed, a triangular fuzzy number concept is adopted to assign priority weights with a view to capture the inherent vagueness in linguistic terms of the decision-maker. In evaluation, if two triangular fuzzy numbers are not intersecting \(({ t}_{11}- { t}_{23}\ge ~0)\), then corresponding degree of possibility value is assumed to be zero (0). However, such situation simply represents the case of one criterion being immensely stronger than other and should not receive a zero value. In this regard, the article proposes an enhanced fuzzy-AHP approach where the triangles are extended about x-axis. This allows developing a mathematical formulation to estimate the true values of height of ordinate (degree of possibility). The empirical study of ranking the SECI modes in the order they influence the performance of the detailed design phase is considered to demonstrate the applicability and usefulness of the proposed framework. In order to measure the performance, five criteria are selected based on a rigorous literature review. After stringent experimentation, it is found that combination and externalization modes highly influence but other modes in order of internalization and socialization loosely have an effect on underlying phase.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, 48021, USA

    Satish Tyagi & Kai Yang

  2. Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA, 70504, USA

    Terrence Chambers

Authors
  1. Satish Tyagi
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  2. Terrence Chambers
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  3. Kai Yang
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Correspondence to Satish Tyagi.

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The authors whose names are listed in the manuscript certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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Communicated by V. Loia.

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Tyagi, S., Chambers, T. & Yang, K. Enhanced fuzzy-analytic hierarchy process. Soft Comput 22, 4431–4443 (2018). https://doi.org/10.1007/s00500-017-2639-y

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