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Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies

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Abstract

Manufacturing organizations often make complex decisions in regards to investment in new manufacturing technologies (NMT). These NMT are assessed based on attributes such as flexibility, quality etc., which are hard to quantify. This fact calls upon the need for a structured decision support systems that can adequately represent qualitative and subjective assessments. The gist of this paper is to propose an integrated fuzzy analytic hierarchy process (AHP) based approach to facilitate the selection and evaluation of NMT in the presence of intangible attributes and uncertainty. Three important issues that are pertinent and critical to fuzzy AHP, namely contradiction in user preferences, deriving priorities from inconsistent fuzzy judgment matrices, and group decision-making are addressed in detail. Also, an attempt is made to compare fuzzy preference programming (FPP) and two-stage logarithmic goal programming (TLGP) based fuzzy prioritization methods. Based on the comparative study, it is concluded that FPP is preferred over TLGP to select and evaluate NMT. Numerical examples are provided to illustrate the aforementioned issues.

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References

  1. Triantaphyllou E (2000) Multi-criteria decision making methods: A comparitive study. Kluwer, Drodrecht

    Google Scholar 

  2. Vaidya OS, Kumar S (2004) Analytic hierarchy process: An overview of application. Eur J Opr Res DOI 10. 1016/j.ejor.2004.04.28

  3. Wabalickis RN (1988) Justification of FMS with the analytic hierarchy process. Manf Sys 17:175−182

    Google Scholar 

  4. Datta VK, Samabasivarao V, Kodali R, Deshmukh SG (1992) Multi-attribute decision model using the analytic hierarchy process for the justification of manufacturing systems. Intl J Prod Res 28:227−234

    Article  Google Scholar 

  5. Sambasivarao KV, Deshmukh SG (1997) A decision support systems for selection and justification of advanced manufacturing technologies. Prod Plan Ctl 8:270−284

    Article  Google Scholar 

  6. Byun BS, Lee KH (2004) A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method. Int J Adv Manf Technol 26:1338−1347

    Article  Google Scholar 

  7. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    MATH  Google Scholar 

  8. Vinod N, Ganesh LS (1996) An empirical analysis of the use of the analytic hierarchy process for estimating membership values in a fuzzy set. Fuz Sets Syst 82:1−16

    Article  Google Scholar 

  9. Belton V, Gear T (1983) On a short-coming of Saaty’s method of analytic hierarchies. Omega 11:228−230

    Article  Google Scholar 

  10. Arbel A, Vargas L (1992) The analytic hierarchy process with interval judgements. In: Goicoechea A et al. (eds). Multiple criteria decision making. Springer, Berlin Heidelberg New York

  11. Van Laarhoven P, Pedrycz W (1983) Fuzzy extension of Saaty’s priority theory. Fuz Sets Syst 11:229−241

    Article  MATH  Google Scholar 

  12. Saaty TL, Vargas L (1987) Uncertainity and rank order in the analytic hierarchy process. Eur J Oper Res 32:107−117

    Article  MATH  MathSciNet  Google Scholar 

  13. Deng H (1999) Multiccriteria analysis with fuzzy pairwise comparison. Int J Approx Reas 21:215−231

    Article  Google Scholar 

  14. Belton V, Pictet J (1997) A framework for group decision using a MCDA model: Sharing, aggregarting or comparing individual information. J Dec Sys 6:283−303

    Google Scholar 

  15. Zahedi F (1986) The analytic hierarchy process – A survey of the method and its applications. Interfaces 16:96−108

    Article  Google Scholar 

  16. Kaufmann A, Gupta MM (1991) Introduction to fuzzy arithmetic:Theory and application. Van Nostrand, New York

    Google Scholar 

  17. Prabhu RT, Vizayakumar K (2001) Technology Choice Using FHDM: A case of iron-making technolgy. IEEE Trans Eng Mgmt 48:209−222

    Article  Google Scholar 

  18. Zhu KJ, Jing Y, Chang DY (1999) A discussion on extent analysis method and applications of fuzzy AHP. Eur J Opr Res 116:450−456

    Article  MATH  Google Scholar 

  19. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuz Sets Syst 17:233−247

    Article  MATH  MathSciNet  Google Scholar 

  20. Falkner CH, Benhajla S (1990) Multi-attribute decision models in the justification of CIM systems. Eng Econ 35:91−113

    Google Scholar 

  21. Udo GJ, Ehie IC (1996) Advanced manufacturing technologies – Determinants of implementation success. Int J Opn Prod Mgt 16:6−26

    Article  Google Scholar 

  22. Small MH (1998) Objectives for adopting advanced manufacturing systems: promises and performance. Ind Mgmt Data Syst 98:129−137

    Article  Google Scholar 

  23. Sambasivarao KV, Deshmukh SG (1995) Selection and implementation of advanced manufacturing technologies –Classification and literature review of issues. Int J Oper Prod Manage 10:43−62

    Article  Google Scholar 

  24. Kwiesielewicz M, van Uden E (2004) Inconsistent and contradictory judgments in pairwise comparison method in the AHP. Comp Opr Res 31:713−719

    Article  MATH  Google Scholar 

  25. Kodali R, Chandra S (2001) Analytic hierarchy process for justification of total productive maintenance. Prod Plan Control 12:695−705

    Article  Google Scholar 

  26. Salo AA (1996) On fuzzy ratio comparisons in hierarchical models. Fuz Sets Syst 84:21−32

    Article  MATH  MathSciNet  Google Scholar 

  27. Leung LC, Cao D (2000) On consistency and ranking of alternatives in fuzzy AHP. Eur J Oper Res 124:102−113

    Article  MATH  Google Scholar 

  28. Mikhailov L (2003) Deriving priorities from fuzzy pairwise comparison judgments. Fuz Sets Syst 134:365−385

    Article  MATH  Google Scholar 

  29. Wang YM, Yang JB, Xu DL (2005) A two-stage logarithmic goal programming method for generating weights from interval comparison matrices. Fuz Sets Syst 152:475−498

    Article  MATH  MathSciNet  Google Scholar 

  30. Forman E, Penewati K (1998) Aggregating induvidual jugements and priorities with the Analytic Hierarchy Process. Eur J Oper Res 108:165−169

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering, Louisiana Tech University, P.O. Box 10348, Ruston, LA, 71272, USA

    Srihari Jaganathan, Jinson J. Erinjeri & Jun-ing Ker

Authors
  1. Srihari Jaganathan
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  2. Jinson J. Erinjeri
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  3. Jun-ing Ker
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Corresponding author

Correspondence to Srihari Jaganathan.

Appendix

Appendix

Definition [24] : The matrix R is called a contradictory if there exists i,j,k=1,2,...,n such that any of the following detailed cases holds:

  1. 1.

    \( r_{{ij}} > 1 \wedge r_{{ik}} < 1 \wedge r_{{jk}} > 1. \)

  2. 2.

    \( r_{{ij}} < 1 \wedge r_{{ik}} > 1 \wedge r_{{jk}} < 1. \)

  3. 3.

    \( r_{{ij}} = 1 \wedge r_{{ik}} > 1 \wedge r_{{jk}} < 1. \)

  4. 4.

    \( r_{{ij}} = 1 \wedge r_{{ik}} < 1 \wedge r_{{jk}} > 1. \)

  5. 5.

    \(r_{{ij}} = 1 \wedge r_{{ik}} = 1 \wedge r_{{jk}} < 1.\)

  6. 6.

    \(r_{{ij}} = 1 \wedge r_{{ik}} = 1 \wedge r_{{jk}} > 1.\)

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Jaganathan, S., Erinjeri, J.J. & Ker, Ji. Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies. Int J Adv Manuf Technol 32, 1253–1262 (2007). https://doi.org/10.1007/s00170-006-0446-1

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  • DOI: https://doi.org/10.1007/s00170-006-0446-1

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