Advertisement

Comparison of multi-criteria decision-making methods for equipment selection

  • Richard Edgar HodgettEmail author
ORIGINAL ARTICLE

Abstract

Equipment selection is a complex task that requires the consideration of multiple criteria with different measurement units. A number of decision-making methods have been proposed for analysing equipment selection problems, each having their own distinctive advantages and limitations. Despite the number of decision-making techniques available, few comparative studies exist that evaluate two or more methods with a singular problem. This paper evaluates three multi-attribute decision-making methods for an equipment selection problem in the early stages of a chemical manufacturing process. A software framework which incorporates analytical hierarchy process (AHP), multi-attribute range evaluations (MARE) and ELimination Et Choix Traduisant la REalité trois (ELECTRE III) was developed and distributed to a technology manager at Fujifilm Imaging Colorants Ltd (FFIC). The manager, within a team of nine people, examined the same decision problem using the three decision analysis methods. The results of the study are examined in respect to assessing each method’s ability to provide accurate representations of the decision-makers’ preferences and the ability to comprehend the uncertainty present. The decision-makers identified MARE as their preferred method, AHP was found to be comparatively more time-consuming and showed the highest variation of results while ELECTRE III was unable to provide a conclusive best result.

Keywords

Multi-criteria decision-making Analytical hierarchy process (AHP) Multi-attribute range evaluations (MARE) ELECTRE III Equipment Selection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chakraborty S, Banik D (2006) Design of a material handling equipment selection model using analytic hierarchy process. Int J Adv Manuf Technol 28:1237–1245CrossRefGoogle Scholar
  2. 2.
    Dehe B, Bamford D (2015) Development, test and comparison of two multiple criteria decision analysis (MCDA) models: a case of healthcare infrastructure location. Expert Syst Appl 42:6717–6727CrossRefGoogle Scholar
  3. 3.
    Brasil Filho AT, Pinheiro PR, Coelho ALV, Costa NC (2009) Comparison of two MCDA classification methods over the diagnosis of Alzheimer’s disease. in: rough sets and knowledge technology. Springer Berlin Heidelberg, pp. 334–341Google Scholar
  4. 4.
    Mulliner E, Malys N, Maliene V (2015) Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, doi: 10.1016/j.omega.2015.05.013
  5. 5.
    Safari H, Faghih A, Fathi MR (2013) Integration of graph theory and matrix approach with fuzzy AHP for equipment selection. J Ind Eng Manag 6(2):477–494Google Scholar
  6. 6.
    Yilmaz B, Dagdeviren M (2011) A combined approach for equipment selection: F-PROMETHEE method and zero–one goal programming. Expert Syst Appl 38:11641–11650CrossRefGoogle Scholar
  7. 7.
    Malczewski J, Rinner C (2015) Dealing with uncertainties. In: Multicriteria decision analysis in geographic information science. s.l.: Springer-Verlag Berlin Heidelberg, 191–221Google Scholar
  8. 8.
    Malczewski J (1999) GIS and multi-criteria decision analysis. Wiley, New YorkGoogle Scholar
  9. 9.
    Saaty TL (1980) The analytic hierarchy process. McGraw Hill InternationalGoogle Scholar
  10. 10.
    Saaty TL (1996) Decision making with dependence and feedback: the analytic network process. RWS Publications, PittsburghGoogle Scholar
  11. 11.
    Zadeh L (1963) Optimality and non-scalar-valued performance. IEEE Trans Autom Control 8(1):59–60CrossRefGoogle Scholar
  12. 12.
    Hwang C-L, Yoon K (1981) Multiple attribute decision making methods and applications. In: a state-of-the-art survey. Springer-Verlag, 181Google Scholar
  13. 13.
    Huang IB, Keisler J, Linkov I (2011) Multi-criteria decision analysis in environmental sciences: ten years of applications and trends. Sci Total Environ 409(19):3578–3594CrossRefGoogle Scholar
  14. 14.
    Smith JE, Winterfeldt DV (2004) Decision analysis in management science. Manag Sci 50(5):561–574CrossRefGoogle Scholar
  15. 15.
    Gass SI (2005) Model world: the great debate—MAUT versus AHP. Interfaces 35(4):308–312CrossRefGoogle Scholar
  16. 16.
    Oliveira M, Fontes DBMM, Pereira T (2014) Multicriteria decision making: a case study in the automobile industry. Ann Manag Sci 3(1)Google Scholar
  17. 17.
    Roy B (1968) Classement et choix en presence de points de vue multiples la methode ELECTRE). La Revue d'Informatique et de Recherche Opérationelle 8:57–75Google Scholar
  18. 18.
    Brans JP (1982) L’ingénierie de la décision: élaboration d’instruments d’aide à la décision. La méthode PROMETHEE. Presses de l’Université LavaGoogle Scholar
  19. 19.
    Salminen P, Hokkanen J, Lahdelma R (1998) Comparing multicriteria methods in the context of environmental problems. Eur J Oper Res 104(3):485–496CrossRefzbMATHGoogle Scholar
  20. 20.
    Hodgett RE (2013) Multi-criteria decision-making in whole process design. PhD Thesis ed. Newcastle universityGoogle Scholar
  21. 21.
    Stewart TJ (2005) Dealing with uncertainties in MCDA. In: Figueira J, Greco S, Ehrgott M (eds) Multi-criteria decision analysis—state of the art annotated surveys. Springer, New YorkGoogle Scholar
  22. 22.
    Farsi JY, Moradi JS, Jamali B (2012) Which product would be chosen? A fuzzy VIKOR method for evaluation and selection of products in terms of customers’ point of view; case study: Iranian cell phone market. Decis Sci Lett 1:23–32CrossRefGoogle Scholar
  23. 23.
    Kahraman C, Onar SC, Oztaysi B (2015) Fuzzy multicriteria decision-making: a literature review. Int J Comput Intel Syst 8(4):637–666CrossRefGoogle Scholar
  24. 24.
    Khandekar AV, Chakraborty S (2015) Selection of industrial robot using axiomatic design principles in fuzzy environment. Dec Sci Lett 4:181–192CrossRefGoogle Scholar
  25. 25.
    Hodgett R, Martin E, Montague G, Talford M (2014) Handling uncertain decisions in whole process design. Prod Plan Control 25(12):1028–1038CrossRefGoogle Scholar
  26. 26.
    Belton V, Stewart T (2010) Chapter 8: problem structuring and multiple criteria decision analysis. In: S Greco, M Ehrgott, JR Figueira, eds. Trends in multiple criteria decision analysis. Springer, pp. 209–239Google Scholar
  27. 27.
    Roy B (1978) ELECTRE III: Un algorithme de classements fondé sur une représentation floue des préférences en présence de critéres multiples. Cahiers du Centre d’Etudes de Recherche Opérationnelle 20:3–24zbMATHGoogle Scholar
  28. 28.
    Millet I, Wedley WC (2003) Modelling risk and uncertainty with the analytic hierarchy process. J Multi-Criteria Decis Anal 11(2):97–107Google Scholar
  29. 29.
    Linkov I, Satterstrom FK, Kiker G, Batchelor C, Bridges T, Ferguson E (2006) From comparative risk assessment to multi-criteria decision analysis and adaptive management: recent developments and applications. Environ Int 32:1072–1093Google Scholar
  30. 30.
    Vohs KD, Baumeister RF, Twenge JM, Schmeichel BJ, Tice DM, Crocker J (2005) Decision fatigue exhausts self-regulatory resources—but so does accommodating to unchosen alternatives. http://www.chicagobooth.edu/research/workshops/marketing/archive/WorkshopPapers/vohs.pdf
  31. 31.
    Salo AA, Hämäläinen RP (1997) On the measurement of preferences in the analytic hierarchy process. J Multi-Criteria Decis Anal 6(6):309–319Google Scholar
  32. 32.
    Meyer P, Bigaret S, Hodgett RE, Olteanu A-L (2015) MCDA: functions to support the multicriteria decision aiding process. http://CRAN.R-project.org/package=MCDA

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Leeds University Business SchoolThe University of LeedsLeedsUK

Personalised recommendations