Annals of Operations Research

, Volume 181, Issue 1, pp 393–421 | Cite as

A soft multi-criteria decision analysis model with application to the European Union enlargement

Article

Abstract

This paper proposes a new multi-criteria decision analysis (MCDA) model that uses a series of existing intuitive and analytical methods to systematically capture both objective and subjective beliefs and preferences from a group of decision makers (DMs). A defuzzification method that combines entropy and the theory of displaced ideal synthesizes crisp values from the DMs’ subjective judgments. This approach assists the DMs in their selection process by plotting alternatives in a four quadrant graph and considering their Euclidean distance from the “ideal” choice. A pilot study illustrates the details of the proposed method. The DMs were a group of graduate students from the University of Paderborn in Germany. The pilot study concerned the addition of new members into the European Union (EU), a decision that has profound economic and political effects on both the entering and existing members of the Union. The DMs were required to consider a large number of internal strengths and weaknesses and external opportunities and threats in assessing the decision to enlarge the EU. Although the pilot study was not performed by actual DMs from the EU, it was an excellent platform for testing the proposed model.

Keywords

Multi-criteria decision analysis Soft computing Fuzzy systems SWOT Analytic hierarchy process European Union enlargement Defuzzification Entropy and theory of displaced ideal 

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References

  1. Abacoumkin, C., & Ballis, A. (2004). Development of an expert system for the evaluation of conventional and innovative technologies in the intermodal transport area. European Journal of Operational Research, 152, 420–436. CrossRefGoogle Scholar
  2. Ali, Y. M., & Zhang, L. (2001). A methodology for fuzzy modeling of engineering systems. Fuzzy Sets and Systems, 118, 181–197. CrossRefGoogle Scholar
  3. Anderson, C., & Vince, J. (2002). Strategic marketing management. Boston: Houghton Mifflin. Google Scholar
  4. Bailey, D., Goonetilleke, A., & Campbell, D. (2003). A new fuzzy multicriteria evaluation method for group site selection in GIS. Journal of Multicriteria Decision Analysis, 12, 337–347. CrossRefGoogle Scholar
  5. Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: an integrated approach. Norwell: Kluwer Academic. Google Scholar
  6. Benoit, J. (1994). Water quality management with imprecise information. European Journal of Operational Research, 76(1), 15–27. CrossRefGoogle Scholar
  7. Buyukozkan, G., & Feyzioglu, O. (2002). A fuzzy logic based decision making approach for new product development. International Journal of Production Economics, 90, 27–45. CrossRefGoogle Scholar
  8. Costa, J. P., Melo, P., Godinho, P., & Dias, L. C. (2003). The AGAP system: a GDSS for project analysis and evaluation. European Journal of Operational Research, 145, 287–303. CrossRefGoogle Scholar
  9. De Luca, A., & Termini, S. (1972). A definition of a non-probabilistic entropy in the setting of fuzzy set theory. Information and Control, 20, 301–312. CrossRefGoogle Scholar
  10. Duarte, C., Ettkin, L. P., Helms, M. M., & Anderson, M. S. (2006). The challenge of Venezuela: a SWOT analysis. Competitiveness Review, 16(3/4), 233–247. Google Scholar
  11. Dubois, D., & Prade, H. (1993). Fuzzy sets and probability: misunderstandings, bridges and gaps. In Proceedings of the second international conference on fuzzy systems (pp. 1059–1068). Google Scholar
  12. Dubois, D., & Prade, H. (2000). Fundamentals of fuzzy sets. Norwel: Kluwer Academic. Google Scholar
  13. Dyer, J. S. (1990a). Remarks on the analytic hierarchy process. Management Science, 36(3), 249–258. CrossRefGoogle Scholar
  14. Dyer, J. S. (1990b). A clarification of ‘Remarks on the analytic hierarchy process’. Management Science, 36(3), 274–275. CrossRefGoogle Scholar
  15. Eleye-Datubo, A. G., Wall, A., & Wang, J. (2008). Marine and offshore safety assessment by incorporative risk modeling in a fuzzy-Bayesian network of an induced mass assignment paradigm. Risk Analysis, 28(1), 95–112. CrossRefGoogle Scholar
  16. Expert Choice [Computer Software] (2006). Decision support software, Inc., McLean, VA. Google Scholar
  17. Festinger, L. (1964). Conflict, decision, and dissonance. London: Tavistock. Google Scholar
  18. Friedlob, G. T., & Schleifer, L. L. F. (1999). Fuzzy logic: application for audit risk and uncertainty. Managerial Auditing Journal, 14(3), 127–137. CrossRefGoogle Scholar
  19. Ghazinoory, S., Esmail Zadeh, A., & Memariani, A. (2007). Fuzzy SWOT analysis. Journal of Intelligent & Fuzzy Systems, 18, 99–108. Google Scholar
  20. Girotra, K., Terwiesch, C., & Ulrich, K. T. (2007). Valuing R&D projects in a portfolio: evidence from the pharmaceutical industry. Management Science, 53, 1452–1466. CrossRefGoogle Scholar
  21. Gouveia, M. C., Dias, L. C., & Antunes, C. H. (2008). Additive DEA based on MCDA with imprecise information. The Journal of the Operational Research Society, 59, 54–63. CrossRefGoogle Scholar
  22. Graves, S. B., & Ringuest, J. L. (1991). Evaluating competing R&D investments. Research-Technology Management, 34(4), 32–36. Google Scholar
  23. Harker, P. T., & Vargas, L. G. (1987). The theory of ratio scale estimation: Saaty’s analytic hierarchy process. Management Science, 33, 1383–1403. CrossRefGoogle Scholar
  24. Harker, P. T., & Vargas, L. G. (1990). Reply to ‘Remarks on the analytic hierarchy process’ by J. S. Dyer. Management Science, 36(3), 269–273. CrossRefGoogle Scholar
  25. Hitt, M. A., Ireland, R. D., & Hoskisson, R. E. (2000). Strategic management: competitiveness and globalization (4th ed.). Cincinnati: South-Western College Publishing. Google Scholar
  26. Ho, W. (2008). Integrated analytic hierarchy process and its applications—a literature review. European Journal of Operational Research, 186, 211–228. CrossRefGoogle Scholar
  27. Hsieh, T.-Y., Lu, S.-T., & Tzeng, G.-H. (2004). Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management, 22, 573–584. CrossRefGoogle Scholar
  28. Jahan-Shahi, H., Shayan, E., & Masood, S. (1999). Cost estimation in flat plate processing using fuzzy sets. Computers & Industrial Engineering, 37(1–2), 485–488. CrossRefGoogle Scholar
  29. Kajanus, M., Kangas, J., & Kurttila, M. (2004). The use of value focused thinking and the A’WOT hybrid method in tourism management. Tourism Management, 25(4), 499–506. CrossRefGoogle Scholar
  30. Kaliszewski, I. (2006). Soft computing for complex multiple criteria decision making. Berlin: Springer. Google Scholar
  31. Kim, S. H., & Ahn, B. S. (1999). Interactive group decision making procedure under incomplete information. European Journal of Operational Research, 116(3), 498–507. CrossRefGoogle Scholar
  32. Kleindorfer, P. R., Kunreuther, H. C., & Schoemaker, P. J. H. (1993). Decision sciences: an integrative perspective. New York: Cambridge University Press. Google Scholar
  33. Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic, theory and applications. Upper Saddle River: Prentice Hall. Google Scholar
  34. Kurttila, M., Pesonen, M., Kangas, J., & Kajanus, M. (2000). Utilizing the analytic hierarchy process (AHP) in SWOT analysis—a hybrid method and its application to a forest-certification case. Forest Policy and Economics, 1(1), 41–52. CrossRefGoogle Scholar
  35. Leyva-Lopez, J. C., & Fernandez-Gonzalez, E. (2003). A new method for group decision support based on ELECTRE III methodology. European Journal of Operational Research, 148, 14–27. CrossRefGoogle Scholar
  36. Liesiö, J., Mild, P., & Salo, A. (2007). Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research, 181, 1488–1505. CrossRefGoogle Scholar
  37. Lin, C., & Hsieh, P. J. (2003). A fuzzy decision support system for strategic portfolio management. Decision Support Systems, 38, 383–398. CrossRefGoogle Scholar
  38. Lootsma, F. A., Mensch, T. C. A., & Vos, F. A. (1990). Multi-criteria analysis and budget reallocation in long-term research planning. European Journal of Operational Research, 47(3), 293–305. CrossRefGoogle Scholar
  39. Masozera, M. K., Alavalapati, J. R. R., Jacobson, S. K., & Shrestha, R. K. (2006). Assessing the suitability of community-based management for the Nyungwe forest reserve, Rwanda. Forest Policy and Economics, 8(2), 206–216. CrossRefGoogle Scholar
  40. Mathieu, R. G., & Gibson, J. E. (1993). A methodology for large-scale R&D planning based on cluster analysis. IEEE Transactions on Engineering Management, 40(3), 283–292. CrossRefGoogle Scholar
  41. Miller, G. A. (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. The Psychological Review, 63, 81–97. CrossRefGoogle Scholar
  42. Mojsilovi, A., Ray, B., Lawrence, R., & Takriti, S. (2007). A logistic regression framework for information technology outsourcing lifecycle management. Computers and Operations Research, 34, 3609–3627. CrossRefGoogle Scholar
  43. Muzzioli, S., & Reynaerts, H. (2007). The solution of fuzzy linear systems by non-linear programming: a financial application. European Journal of Operational Research, 177(2), 1218–1231. CrossRefGoogle Scholar
  44. Novicevic, M. M., Harvey, M., Autry, C. W., & Bond, E. U., III (2004). Dual-perspective SWOT: a synthesis of marketing intelligence and planning. Marketing Intelligence & Planning, 22(1), 84–94. CrossRefGoogle Scholar
  45. Osawa, Y., & Murakami, M. (2002). Development and application of a new methodology of evaluating industrial R&D projects. Research & Development Management, 32, 79–85. Google Scholar
  46. Paisittanand, S., & Olson, D. L. (2006). A simulation study of IT outsourcing in the credit card business. European Journal of Operational Research, 175, 1248–1261. CrossRefGoogle Scholar
  47. Panagiotou, G. (2003). Bringing SWOT into focus. Business Strategy Review, 14(2), 8–10. CrossRefGoogle Scholar
  48. Pap, E., Bosnjak, Z., & Bosnjak, S. (2000). Application of fuzzy sets with different t-norms in the interpretation of portfolio matrices in strategic management. Fuzzy Sets and Systems, 114, 123–131. CrossRefGoogle Scholar
  49. Poyhonen, M. A., Hamalainen, R. P., & Salo, A. A. (1997). An experiment on the numerical modelling of verbal ratio statements. Journal of Multi-Criteria Decision Analysis, 6(1), 1–10. CrossRefGoogle Scholar
  50. Rolly Intan, R., & Mukaidono, M. (2004). Fuzzy conditional probability relations and their applications in fuzzy information systems. Knowledge and Information Systems, 6(3), 345–365. CrossRefGoogle Scholar
  51. Roychowdhury, S., & Pedrycz, W. (2001). A survey of defuzzification strategies. International Journal of Intelligent Systems, 16, 679–695. CrossRefGoogle Scholar
  52. Runkler, T. A. (1996). Extended defuzzification methods and their properties. IEEE Transactions, 694–700. Google Scholar
  53. Saaty, T. L. (2003). Decision-making with the AHP: why is the principal eigenvector necessary? European Journal of Operational Research, 145(1), 85–91. CrossRefGoogle Scholar
  54. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234–281. CrossRefGoogle Scholar
  55. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill. Google Scholar
  56. Saaty, T. L. (1989). Decision making, scaling, and number crunching. Decision Sciences, 20, 404–409. CrossRefGoogle Scholar
  57. Saaty, T. L. (1990a). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48, 9–26. CrossRefGoogle Scholar
  58. Saaty, T. L. (1990b). An exposition of the AHP in reply to the paper ‘Remarks on the analytic hierarchy process’. Management Science, 36(3), 259–268. CrossRefGoogle Scholar
  59. Saaty, T. L. (2006). Fundamentals of decision making and priority theory with the analytic hierarchy process. Pittsbutgh: RWS. Google Scholar
  60. Saaty, T. L., & Sodenkamp, M. (2008). Making decisions in hierarchic and network systems. International Journal of Applied Decision Sciences, 1(1), 24–79. CrossRefGoogle Scholar
  61. Saaty, T. L., & Tran, L. T. (2007). On the invalidity of fuzzifying numerical judgments in the analytic hierarchy process. Mathematical and Computer Modelling, 46(7–8), 962–975. CrossRefGoogle Scholar
  62. Schelling, T. C. (1960). The strategy of conflict. Cambridge: Harvard University Press. Google Scholar
  63. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656. Google Scholar
  64. Shinno, H., Yoshioka, H., Marpaung, S., & Hachiga, S. (2006). Quantitative SWOT analysis on global competitiveness of machine tool industry. Journal of Engineering Design, 17(3), 251–258. CrossRefGoogle Scholar
  65. Shrestha, R. K., Alavalapati, J. R. R., & Kalmbacher, R. S. (2004). Exploring the potential for silvopasture adoption in south-central Florida: an application of SWOT-AHP method. Agricultural Systems, 81(3), 185–199. CrossRefGoogle Scholar
  66. Slyeptsov, A. I., & Sodenkamp, M. A. (2007). Decision making in complex systems. Kyjiv: National Pedagogical Dragomanov University. Google Scholar
  67. Sodenkamp, M. A. (2005). Soft models of SWOT-analysis on the base of pairwise comparisons networks. Bulletin of Donetsk University, Series A: Natural sciences, 2, 375–380. Google Scholar
  68. Tavana, M. (2006). A priority assessment multi-criteria decision model for human spaceflight mission planning at NASA. Journal of the Operational Research Society, 57, 1197–1215. CrossRefGoogle Scholar
  69. Tavana, M. (2004). A subjective assessment of alternative mission architectures for the human exploration of Mars at NASA using multicriteria decision making. Computers and Operations Research, 31, 1147–1164. CrossRefGoogle Scholar
  70. Tavana, M. (2002). Euclid: strategic alternative assessment matrix. Journal of Multi-Criteria Decision Analysis, 11, 75–96. CrossRefGoogle Scholar
  71. Tavana, M., & Banerjee, S. (1995). Strategic assessment model (SAM): a multiple criteria decision support system for evaluation of strategic alternatives. Decision Sciences, 26, 119–143. CrossRefGoogle Scholar
  72. Tavana, M., Bourgeois, B., & Sodenkamp, M. (2009). Fuzzy multiple criteria base realignment and closure (BRAC) benchmarking system at the department of defense. Benchmarking: An International Journal, 16(2), 192–222. CrossRefGoogle Scholar
  73. Tavana, M., & Sodenkamp, M. A. (2009). A fuzzy multi-criteria decision analysis model for advanced technology assessment at Kennedy space center. Journal of the Operational Research Society. doi:10.1057/jors.2009.107, advance online publication. Google Scholar
  74. Triantaphyllou, E. (2000). Multi-criteria decision making methods: a comparative study. Boston: Kluwer Academic. Google Scholar
  75. Triantaphyllou, E., & Baig, K. (2005). The impact of aggregating benefit and cost criteria in four MCDA methods. IEEE Transactions on Engineering Management, 52, 213–226. CrossRefGoogle Scholar
  76. Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: an overview of applications. European Journal of Operational Research, 169, 1–29. CrossRefGoogle Scholar
  77. Valentin, E. K. (2001). SWOT analysis from a resource-based view. Journal of Marketing Theory and Practice, 9(2), 54–68. Google Scholar
  78. Valls, A., & Torra, V. (2000). Using classification as an aggregation tool in MCDM. Fuzzy Sets and Systems, 15(1), 159–168. CrossRefGoogle Scholar
  79. Van Leekwijk, W., & Kerre, E. E. (1999). Defuzzification: criteria and classification. Fuzzy Sets and Systems, 108(2), 159–178. CrossRefGoogle Scholar
  80. Walters-York, M., & Curatola, A. P. (2000). Theoretical reflections on the use of students as surrogate subjects in behavioral experimentation. Advances in Accounting Behavioral Research, 3, 243–263. CrossRefGoogle Scholar
  81. Wang, J., & Hwang, W.-L. (2007). A fuzzy set approach for R&D portfolio selection using a real options valuation model. Omega, 35, 247–257. CrossRefGoogle Scholar
  82. Yang, J. B., & Xu, D. L. (2002). On the evidential reasoning algorithm for multiattribute decision analysis under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 32(3), 289–304. CrossRefGoogle Scholar
  83. Zadeh, L. A. (1998). Roles of soft computing and fuzzy logic in the conception, design and deployment of information/intelligent systems. In Computational intelligence: soft computing and fuzzy-neuro integration with applications (pp. 1–9). Google Scholar
  84. Zadeh, L. A. (1999). From computing with numbers to computing with words, from manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems, 45(1), 105–119. Google Scholar
  85. Zeleny, M. A. (1974). Concept of compromise solutions and the method of the disptaced ideal. Computers and Operations Research, 1(3–4), 479–496. CrossRefGoogle Scholar
  86. Zeleny, M. A. (1982). Multiple criteria decision making. New York: McGraw-Hill. Google Scholar
  87. Zopounidis, C., & Doumpos, M. (2001). Multicriteria decision aid in uncertainty and financial risk management. In J. Gil-Aluja (Ed.), Handbook of management under uncertainty. Dordrecht: Kluwer Academic. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Madjid Tavana
    • 1
  • Mariya A. Sodenkamp
    • 2
  • Leena Suhl
    • 3
  1. 1.Management Information Systems, Lindback Distinguished Chair of Information SystemsLa Salle UniversityPhiladelphiaUSA
  2. 2.Business Information Systems Department, Faculty of Business Administration and EconomicsUniversity of PaderbornRuethen-OestereidenGermany
  3. 3.Business Information Systems, Faculty of Business Administration and EconomicsUniversity of PaderbornPaderbornGermany

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