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

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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.

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References

  • 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.

    Article  Google Scholar 

  • Ali, Y. M., & Zhang, L. (2001). A methodology for fuzzy modeling of engineering systems. Fuzzy Sets and Systems, 118, 181–197.

    Article  Google Scholar 

  • Anderson, C., & Vince, J. (2002). Strategic marketing management. Boston: Houghton Mifflin.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: an integrated approach. Norwell: Kluwer Academic.

    Google Scholar 

  • Benoit, J. (1994). Water quality management with imprecise information. European Journal of Operational Research, 76(1), 15–27.

    Article  Google Scholar 

  • Buyukozkan, G., & Feyzioglu, O. (2002). A fuzzy logic based decision making approach for new product development. International Journal of Production Economics, 90, 27–45.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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).

  • Dubois, D., & Prade, H. (2000). Fundamentals of fuzzy sets. Norwel: Kluwer Academic.

    Google Scholar 

  • Dyer, J. S. (1990a). Remarks on the analytic hierarchy process. Management Science, 36(3), 249–258.

    Article  Google Scholar 

  • Dyer, J. S. (1990b). A clarification of ‘Remarks on the analytic hierarchy process’. Management Science, 36(3), 274–275.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Expert Choice [Computer Software] (2006). Decision support software, Inc., McLean, VA.

  • Festinger, L. (1964). Conflict, decision, and dissonance. London: Tavistock.

    Google Scholar 

  • Friedlob, G. T., & Schleifer, L. L. F. (1999). Fuzzy logic: application for audit risk and uncertainty. Managerial Auditing Journal, 14(3), 127–137.

    Article  Google Scholar 

  • Ghazinoory, S., Esmail Zadeh, A., & Memariani, A. (2007). Fuzzy SWOT analysis. Journal of Intelligent & Fuzzy Systems, 18, 99–108.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Graves, S. B., & Ringuest, J. L. (1991). Evaluating competing R&D investments. Research-Technology Management, 34(4), 32–36.

    Google Scholar 

  • Harker, P. T., & Vargas, L. G. (1987). The theory of ratio scale estimation: Saaty’s analytic hierarchy process. Management Science, 33, 1383–1403.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hitt, M. A., Ireland, R. D., & Hoskisson, R. E. (2000). Strategic management: competitiveness and globalization (4th ed.). Cincinnati: South-Western College Publishing.

    Google Scholar 

  • Ho, W. (2008). Integrated analytic hierarchy process and its applications—a literature review. European Journal of Operational Research, 186, 211–228.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kaliszewski, I. (2006). Soft computing for complex multiple criteria decision making. Berlin: Springer.

    Google Scholar 

  • Kim, S. H., & Ahn, B. S. (1999). Interactive group decision making procedure under incomplete information. European Journal of Operational Research, 116(3), 498–507.

    Article  Google Scholar 

  • Kleindorfer, P. R., Kunreuther, H. C., & Schoemaker, P. J. H. (1993). Decision sciences: an integrative perspective. New York: Cambridge University Press.

    Google Scholar 

  • Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic, theory and applications. Upper Saddle River: Prentice Hall.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Liesiö, J., Mild, P., & Salo, A. (2007). Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research, 181, 1488–1505.

    Article  Google Scholar 

  • Lin, C., & Hsieh, P. J. (2003). A fuzzy decision support system for strategic portfolio management. Decision Support Systems, 38, 383–398.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Panagiotou, G. (2003). Bringing SWOT into focus. Business Strategy Review, 14(2), 8–10.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Roychowdhury, S., & Pedrycz, W. (2001). A survey of defuzzification strategies. International Journal of Intelligent Systems, 16, 679–695.

    Article  Google Scholar 

  • Runkler, T. A. (1996). Extended defuzzification methods and their properties. IEEE Transactions, 694–700.

  • Saaty, T. L. (2003). Decision-making with the AHP: why is the principal eigenvector necessary? European Journal of Operational Research, 145(1), 85–91.

    Article  Google Scholar 

  • Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234–281.

    Article  Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.

    Google Scholar 

  • Saaty, T. L. (1989). Decision making, scaling, and number crunching. Decision Sciences, 20, 404–409.

    Article  Google Scholar 

  • Saaty, T. L. (1990a). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48, 9–26.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Saaty, T. L. (2006). Fundamentals of decision making and priority theory with the analytic hierarchy process. Pittsbutgh: RWS.

    Google Scholar 

  • Saaty, T. L., & Sodenkamp, M. (2008). Making decisions in hierarchic and network systems. International Journal of Applied Decision Sciences, 1(1), 24–79.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Schelling, T. C. (1960). The strategy of conflict. Cambridge: Harvard University Press.

    Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Slyeptsov, A. I., & Sodenkamp, M. A. (2007). Decision making in complex systems. Kyjiv: National Pedagogical Dragomanov University.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Tavana, M. (2002). Euclid: strategic alternative assessment matrix. Journal of Multi-Criteria Decision Analysis, 11, 75–96.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Triantaphyllou, E. (2000). Multi-criteria decision making methods: a comparative study. Boston: Kluwer Academic.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: an overview of applications. European Journal of Operational Research, 169, 1–29.

    Article  Google Scholar 

  • Valentin, E. K. (2001). SWOT analysis from a resource-based view. Journal of Marketing Theory and Practice, 9(2), 54–68.

    Google Scholar 

  • Valls, A., & Torra, V. (2000). Using classification as an aggregation tool in MCDM. Fuzzy Sets and Systems, 15(1), 159–168.

    Article  Google Scholar 

  • Van Leekwijk, W., & Kerre, E. E. (1999). Defuzzification: criteria and classification. Fuzzy Sets and Systems, 108(2), 159–178.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

  • 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 

  • Zeleny, M. A. (1974). Concept of compromise solutions and the method of the disptaced ideal. Computers and Operations Research, 1(3–4), 479–496.

    Article  Google Scholar 

  • Zeleny, M. A. (1982). Multiple criteria decision making. New York: McGraw-Hill.

    Google Scholar 

  • 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 

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Tavana, M., Sodenkamp, M.A. & Suhl, L. A soft multi-criteria decision analysis model with application to the European Union enlargement. Ann Oper Res 181, 393–421 (2010). https://doi.org/10.1007/s10479-010-0727-9

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