Advertisement

Understanding the Effect of Assignment of Importance Scores of Evaluation Criteria Randomly in the Application of DOE-TOPSIS in Decision Making

  • Yusuf Tansel İç
  • Mustafa Yurdakul
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 793)

Abstract

In conventional applications of hybrid DoE- TOPSIS technique in decision making problems, full factorial design layouts are generally used because of their ability to measure the effects of all possible combinations for evaluation factors. In a typical application, for a design layout, a number of replications are generated by assigning different sets of relative importance scores for evaluation factors. A TOPSIS score is then obtained for each experiment and replication pair. Regression analysis is finally applied to obtain a relationship with inputs (values of evaluation factors) and outputs (alternatives’ TOPSIS meta-model scores). The key in conventional application of hybrid DoE-TOPSIS technique is generation of relative importance scores. Each set of scores can be assigned by a decision maker or generated randomly. This paper aims to determine whether using either of the two methods in determination of relative importance scores makes any difference in the ranking orders of alternatives.

Keywords

Decision-making Design of Experiment (DoE) TOPSIS DoE-TOPSIS 

References

  1. 1.
    Ic, Y.T.: An experimental design approach using TOPSIS method for the selection of computer- integrated manufacturing technologies. Robot. Comput. Integr. Manuf. 28, 245–256 (2012)CrossRefGoogle Scholar
  2. 2.
    Ic, Y.T.: A TOPSIS based design of experiment approach to assess company ranking. Appl. Math. Comput. 227, 630–647 (2014)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Ic, Y.T.: Development of a new multi-criteria optimization method for engineering design problems. Res. Eng. Des. 27(4), 413–436 (2016)CrossRefGoogle Scholar
  4. 4.
    Sabaghi, M., Mascle, C., Baptiste, P.: Evaluation of products at design phase for an efficient disassembly at end-of-life. J. Clean. Prod. 116, 177–186 (2016)CrossRefGoogle Scholar
  5. 5.
    DeVor, R.E., Chang, T.-H., Sutherland, J.W.: Statistical Quality Design and Control-Contemporary Concepts and Methods. Prentice-Hall, Inc, New Jersey (1992)Google Scholar
  6. 6.
    Saaty, T.L.: Fundamentals of Decision Making with the Analytic Hierarchy Process. In: The Analytic Hierarcy Process Series, vol. 6. RWS Publications, Pittsburg (2006)Google Scholar
  7. 7.
    Parkan, C., Wu, M.-L.: Decision Making and Performance Measurement Models with Applications to Robot Selection. Comput. Ind. Eng. 36, 503–523 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial Engineering, Faculty of EngineeringBaskent UniversityBaglica, EtimesgutTurkey
  2. 2.Department of Mechanical Engineering, Faculty of EngineeringGazi UniversityMaltepeTurkey

Personalised recommendations