Advertisement

Abstract

The Euclidean distance is an established concept in the field of Mathematics [1, 2]. The weighted Euclidean distance-based approach (WEDBA) is based on the weighted distance of alternatives from the most and least favorable situations, respectively.

Keywords

Flexible Manufacturing System Decision Matrix Objective Weight Analytic Hierarchy Process Method Product Flexibility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Dattorro J (2008) Convex optimization and Euclidean distance geometry. Meboo Publishing, CaliforniaGoogle Scholar
  2. 2.
    Gower JC (1982) Euclidean distance geometry. Math Sci 7:1–14MathSciNetMATHGoogle Scholar
  3. 3.
    Rao RV, Singh D (2011) Evaluating flexible manufacturing systems using Euclidean distance-based integrated approach. Int J Decis Sci Risk Manag 3:32–53Google Scholar
  4. 4.
    Rao RV, Singh D (2012) Weighted Euclidean distance based approach as a multiple attribute decision making method for plant or facility layout design selection. Int J Ind Eng Comput 3(3):365–382Google Scholar
  5. 5.
    Rao RV, Singh D, Bleicher F, Dorn C (2012) Weighted Euclidean distance based approach as a multiple attribute decision making method for manufacturing situations. Int J Multicriteria Decision Making (in press)Google Scholar
  6. 6.
    Rao RV (2007) Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. Springer, LondonGoogle Scholar
  7. 7.
    Saaty TL (2000) Fundamentals of decision making and priority theory with AHP. RWS Publications, PittsburgGoogle Scholar
  8. 8.
    Edwards W, Newman JR (1986) Multiattribute evaluation. In: Arkes HR, Hammond KR (eds) Judgment and decision making: an interdisciplinary reader. Cambridge University Press, CambridgeGoogle Scholar
  9. 9.
    Manshadi BD, Mahmudi H, Abedian A, Mahmudi R (2007) A novel method for materials selection in mechanical design: combination of non-linear normalization and a modified digital logic method. Mater Des 28:8–15CrossRefGoogle Scholar
  10. 10.
    Jee DH, Kang KJ (2000) A method for optimal material selection aided with decision making theory. Mater Des 21(3):199–206CrossRefGoogle Scholar
  11. 11.
    Chatterjee P, Athawale VM, Chakraborty S (2009) Selection of materials using compromise ranking and outranking methods. Mater Des 30:4043–4053CrossRefGoogle Scholar
  12. 12.
    Jahan A, Ismail MY, Mustapha F, Sapuan SM (2010) Material selection based on ordinal data. Mater Des 31(7):3180–3187CrossRefGoogle Scholar
  13. 13.
    Rao RV, Padmanabhan KK (2006) Selection, identification and comparison of industrial robots using digraph and matrix methods. Robot Comput Integr Manuf 22:373–383CrossRefGoogle Scholar
  14. 14.
    Shih HS (2008) Incremental analysis for MCDM with an application to group TOPSIS. Eur J Oper Res 186:720–734MATHCrossRefGoogle Scholar
  15. 15.
    Chatterjee P, Athawale VM, Chakraborty S (2010) Selection of industrial robots using compromise ranking and outranking methods. Robot Comput Integr Manuf 26:483–489CrossRefGoogle Scholar
  16. 16.
    Rao RV, Patel BK, Parnichkun M (2011) Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robot Auton Syst 59:367–375CrossRefGoogle Scholar
  17. 17.
    Bhangale PP, Agrawal VP, Saha SK (2004) Attribute based specification, comparison and selection of a robot. Mech Mach Theory 39:1345–1366MATHCrossRefGoogle Scholar
  18. 18.
    Priore P, Fuente D, Puente J, Parreno J (2006) A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Eng Appl Artif Intell 19(3):247–255CrossRefGoogle Scholar
  19. 19.
    Karsak EE (2008) Using data envelopment analysis for evaluating flexible manufacturing systems in the presence of imprecise data. Int J Adv Manuf Technol 35:867–874CrossRefGoogle Scholar
  20. 20.
    Rao RV (2009) Flexible manufacturing system selection using an improved compromise ranking method. Int J Ind Syst Eng 4(2):198–215Google Scholar
  21. 21.
    Sarkis J (1997) Evaluating flexible manufacturing systems using data envelopment analysis. Eng Econ 43:25–46CrossRefGoogle Scholar
  22. 22.
    Rao RV (2006) A material selection model using graph theory and matrix approach. Mater Sci Eng A 431:248–255CrossRefGoogle Scholar
  23. 23.
    Rao RV (2006) A decision making framework model for evaluating flexible manufacturing systems using digraph and matrix methods. Int J Adv Manuf Technol 30:1101–1110CrossRefGoogle Scholar
  24. 24.
    Rao RV (2006) Machine group selection in a flexible manufacturing cell using digraph and matrix methods. Int J Ind Syst Eng 1(4):502–518Google Scholar
  25. 25.
    Rao RV (2008) A decision making methodology for material selection using an improved compromise ranking method. Mater Des 29:1949–1954CrossRefGoogle Scholar
  26. 26.
    Rao RV (2008) Evaluation of environmentally conscious manufacturing programs using multiple attribute decision making methods. Proc Inst Mech Eng, Part B: J Eng Manuf 222(3):441–451CrossRefGoogle Scholar
  27. 27.
    Rao RV (2008) Evaluating flexible manufacturing systems using a combined multiple attribute decision making method. Int J Prod Res 46(7):1975–1989MATHCrossRefGoogle Scholar
  28. 28.
    Karsak EE, Kuzgunkaya O (2002) A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system. Int J Prod Econ 79:101–111CrossRefGoogle Scholar
  29. 29.
    Rao RV, Parnichkun M (2009) Flexible manufacturing system selection using a combinatorial mathematics-based decision making method. Int J Prod Res 47(24):6981–6998MATHCrossRefGoogle Scholar
  30. 30.
    Sivapirakasam SP, Mathew J, Surianarayanan M (2011) Multi-attribute decision making for green electrical discharge machining. Expert Syst Appl 38:8370–8374CrossRefGoogle Scholar
  31. 31.
    Bufardi A, Gjeorghe R, Kiritsis D, Xirouchakis P (2003) Multi-criteria decision aid approach for product end-of-life alternative selection. Int J Prod Res 42:3139–3157CrossRefGoogle Scholar
  32. 32.
    Rao RV, Patel BK (2010) Decision making in the manufacturing environment using an improved PROMETHEE method. Int J Prod Res 48:4665–4682MATHCrossRefGoogle Scholar
  33. 33.
    Rao RV, Patel BK (2010) A subjective and objective integrated multiple attribute decision making method for material selection. Mater Des 31(10):4738–4747CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentS.V. National Institute of TechnologySuratIndia

Personalised recommendations