Application of Analytic Hierarchy Process for the Selection of Cotton Fibers

  • Published : 2004.12.01

Abstract

In many engineering applications, the final decision is based on the evaluation of a number of alternatives in terms of a number of criteria. This problem may become very intricate when the selection criteria are expressed in terms of different units or the pertinent data are difficult to be quantified. The Analytic Hierarchy Process (AHP) is an effective way in dealing with such kind of complicated problems. Cotton fiber is selected or graded, in the spinning industries, based on several quality criteria. However, the existing selection or grading method based on Fiber quality Index (FqI) is rather crude and ambiguous. This paper presents a novel approach of cotton fiber selection using the AHP methodology of Multi Criteria Decision Making.

Keywords

References

  1. USTER News Bulletin, 38, 23 (1991)
  2. A. Arbel and Y. E. Orgler, European J. of Operational Res., 48, 27 (1990) https://doi.org/10.1016/0377-2217(90)90058-J
  3. V. S Lai, B. K. Wong, and W. Cheung, European J. of Operational Res., 137, 134 (2002) https://doi.org/10.1016/S0377-2217(01)00084-4
  4. P. R. Drake, Int. J. of Eng. Ed., 14(3), 191 (1998)
  5. J. Korpela and M. Tuominen, IEEE Transaction on Engg. Management, 43(3), 323 (1996) https://doi.org/10.1109/17.511842
  6. H. V. Sreenivasa Murthy and S. K. Samanta, Indian Text. J., 111(3), 29 (2000)
  7. Z. Hongwei, Int. Text. Bulletin, 1, 44 (2003)
  8. A. Guha, Ph. D Thesis, I. I. T. Delhi, 2001
  9. T. L Saaty, 'The Analytic Hierarchy Process', McGraw-Hill, New York, 1980
  10. T. L. Saaty, European J. of Operational Res., 48, 9 (1990) https://doi.org/10.1016/0377-2217(90)90057-I
  11. T. L. Saaty, IEEE Transaction on Engg. Management, 30(3), 140 (1983)
  12. T. L. Saaty, Management Sci., 32(7), 841 (1986) https://doi.org/10.1287/mnsc.32.7.841
  13. T. L. Saaty, European J. of Operational Res., 74, 426 (1994) https://doi.org/10.1016/0377-2217(94)90222-4
  14. T. L. Saaty, Management Sci., 36(3), 259 (1990) https://doi.org/10.1287/mnsc.36.3.259
  15. J. S. Dyer, Management Sci., 36(3), 249 (1990) https://doi.org/10.1287/mnsc.36.3.249
  16. P. T. Harker and L. G. Vargas, Management Sci., 36(3), 269 (1990) https://doi.org/10.1287/mnsc.36.3.269
  17. J. S. Dyer, Management Sci., 36(3), 274 (1990) https://doi.org/10.1287/mnsc.36.3.274
  18. P. T. Harker and L. G. Vargas, Management Sci., 33(11), 1383 (1987) https://doi.org/10.1287/mnsc.33.11.1383
  19. V. Belton and T. Gear, Omega, 11, 228 (1983) https://doi.org/10.1016/0305-0483(83)90047-6
  20. S. Schenkerman, European J. of Operational Res., 74, 407 (1994) https://doi.org/10.1016/0377-2217(94)90220-8
  21. G. L. Vargas, European J. of Operational Res., 74, 420 (1994) https://doi.org/10.1016/0377-2217(94)90221-6
  22. E. Triantaphyllou and S. H. Maan, Decision Support Systems, 5, 303 (1989) https://doi.org/10.1016/0167-9236(89)90037-7
  23. E. Triantaphyllou and S. H. Maan, Int. J. of Industrial Engg.: Applications and Practice, 2(1), 35 (1995)
  24. M. J. Liberatore, IEEE Transaction on Engg. Management, 34(1), 12 (1987)