Fuzzy Optimization and Decision Making

, Volume 9, Issue 4, pp 455–492 | Cite as

A fuzzy opportunity and threat aggregation approach in multicriteria decision analysis

  • Madjid Tavana
  • Mariya A. Sodenkamp
  • Mohsen Pirdashti
Article

Abstract

Economic expansion in developed countries coupled with dramatically growing economies in countries such as China and India have precipitated a steady increase in demand for oil and natural gas. The Caspian Sea region holds large quantities of both oil and natural gas. Because the Caspian Sea is landlocked and the region’s nations are distant from the largest energy markets, transportation must at least begin by pipeline. While some lines currently exist, pipelines with the capacity of transporting larger amounts of energy resources must be constructed to meet the global demand. This study is conducted for a multinational oil and natural gas producer to develop a multicriteria decision analysis (MCDA) framework for evaluating five possible pipeline routes in the Caspian Sea region. The proposed MCDA model considers a large number of conflicting criteria in the evaluation process and captures decision makers’ (DMs’) beliefs through a series of intuitive and analytical methods such as the analytic network process and fuzzy scoring. A defuzzification method is used to obtain crisp values from the subjective judgments and estimates provided by multiple DMs. These crisp values are aggregated and synthesized with the concept of entropy and the theory of the displaced ideal. The alternative routes are plotted on a diagram in a polar coordinate system and a classification scheme is used along with the Euclidean distance to measure which alternative is closer to the ideal route.

Keywords

Multi-criteria decision analysis Group decision making Analytic network process Fuzzy scoring Level-2 fuzzy sets Defuzzification Entropy 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: 420–436CrossRefGoogle Scholar
  2. Bouyssou, D. (1989). Problemes de construction de criteres. Cahier du LAMSADE, 91.Google Scholar
  3. Bouyssou D. (1990) Building criteria: A prerequisite for MCDA. In: Bana e Costa C. A. (eds) Readings in MCDA. Springer, HeidelbergGoogle Scholar
  4. Chen, S. J., & Hwang, C. (1992). Fuzzy multiple attribute decision making methods and applications. In Lecture Notes in Economics and Mathematical Systems, No. 375, New York: Springer.Google Scholar
  5. Chen C.-T., Cheng H.-L. (2009) A comprehensive model for selecting information system projects under fuzzy environment. International Journal of Project Management 27(4): 389–399CrossRefGoogle Scholar
  6. Chou S. Y., Chang Y. H., Shen C. Y. (2008) A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research 189(1): 132–145MATHCrossRefGoogle Scholar
  7. Cooper R. G. (1992) The NewProd system: The industry experience. Journal of Product Innovation Management 9: 113–127CrossRefGoogle Scholar
  8. Cooper R. G., Edgett S. J., Kleinschmidt E. J. (1999) New product portfolio management: Practices and performance. Journal of Product Innovation Management 16: 333–351CrossRefGoogle Scholar
  9. De Luca A., Termini S. (1972) A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and Control 20: 301–312MATHCrossRefMathSciNetGoogle Scholar
  10. De Kluyver C. A., Moskowitz H. (1984) Assessing scenario probabilities via interactive goal programming. Management Science 30(3): 273–278MATHCrossRefGoogle Scholar
  11. De Tré G., De Caluwe R. (2003) Level 2 fuzzy sets and their usefulness in object-oriented database modeling. Fuzzy Sets and Systems 140: 29–49MATHCrossRefMathSciNetGoogle Scholar
  12. Deng H. (2009) Developments in fuzzy multicriteria analysis. Fuzzy Information and Engineering 1(1): 103–109CrossRefGoogle Scholar
  13. Dimova L., Sevastianov P., Sevastianov D. (2006) MCDM in a fuzzy setting: Investment projects assessment application. International Journal of Production Economics 100: 10–29CrossRefGoogle Scholar
  14. Dubois D., Prade H. (1980) Fuzzy sets and systems: Theory and applications. Academic Press, New YorkMATHGoogle Scholar
  15. Dubois D., Prade H. (2000) Fundamentals of fuzzy sets. Kluwer, BostonMATHGoogle Scholar
  16. Felsenthal D. S., Machover M. (2004) A priori voting power: What is it all about? Political Studies Review 2: 1–23CrossRefGoogle Scholar
  17. Girotra K., Terwiesch C., Ulrich K. T. (2007) Valuing R&D projects in a portfolio: Evidence from the pharmaceutical industry. Management Science 53: 1452–1466CrossRefGoogle Scholar
  18. Graves S. B., Ringuest J. L. (1991) Evaluating competing R&D investments. Research-Technology Management 34(4): 32–36Google Scholar
  19. Hazelrigg G. A. Jr, Huband F. L. (1985) RADSIM—A methodology for large-scale R&D program assessment. IEEE Transactions on Engineering Management 32(3): 106–116Google Scholar
  20. Hites R., De Smet Y., Risse N., Salazar-Neumann M., Vincke P. (2006) About the applicability of MCDA to some robustness problems. European Journal of Operational Research 174(1): 322–332MATHCrossRefGoogle Scholar
  21. Huang C.-C., Chu P.-Y., Chiang Y.-H. (2008) A fuzzy AHP application in government-sponsored R&D project selection. Omega 36(6): 1038–1052CrossRefGoogle Scholar
  22. Klare M. T. (2003) The empire’s new frontiers. Current History 102: 383–387Google Scholar
  23. Laruelle, A., & Widgren, M. (2000). Voting power in a sequence of cooperative games: The case of EU procedures, Homo Oeconomicus XVII, pp. 67–84. Reprint in Holler, M. J. and G. Owen (Eds.), 2001, Power Indices and Coalition Formation (pp. 253–271), Netherlands: Kluwer.Google Scholar
  24. 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): 293–305CrossRefGoogle Scholar
  25. Mandakovic T., Souder W. E. (1985) An interactive decomposable heuristic for project selection. Management Science 31(10): 1257–1271CrossRefGoogle Scholar
  26. 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): 283–292CrossRefGoogle Scholar
  27. Mehrez A. (1988) Selecting R&D projects: A case study of the expected utility approach. Technovation 8(4): 299–311CrossRefGoogle Scholar
  28. Mojsilovi A., Ray B., Lawrence R., Takriti S. (2007) A logistic regression framework for information technology outsourcing lifecycle management. Computers and Operations Research 34: 3609–3627CrossRefGoogle Scholar
  29. Moore J. R., Baker N. R. (1969) An analytical approach to scoring model design—Application to research and development project selection. IEEE Transactions on Engineering Management 16(3): 90–98Google Scholar
  30. Osawa Y. (2003) How well did the new Sumitomo Electric project ranking method predict performance? Research & Development Management 33: 343–350Google Scholar
  31. Osawa Y., Murakami M. (2002) Development and application of a new methodology of evaluating industrial R&D projects. Research & Development Management 32: 79–85Google Scholar
  32. Paisittanand S., Olson D. L. (2006) A simulation study of IT outsourcing in the credit card business. European Journal of Operational Research 175: 1248–1261MATHCrossRefGoogle Scholar
  33. Roy B. (1975) Vers une methodologie generale d.aide a la decision . Metra 14(3): 59–497Google Scholar
  34. Roy B. (1985) Methodologie Multicritiere d”Aide a la decision. Economica, ParisGoogle Scholar
  35. Roy, B., & Bouyssou, D. (1987). Famille de critères: Problème de cohérence et de dépendence. Decument du Lamsade 37, Universite Paris-Dauphine.Google Scholar
  36. Roy B., Bouyssou D. (1993) Decision-aid: An elementary introduction with emphasis on multiple criteria. Investigacion Operativa 3: 175–190Google Scholar
  37. Roychowdhury S., Pedrycz W. (2001) A survey of defuzzification strategies. International Journal of Intelligent Systems 16: 679–695MATHCrossRefGoogle Scholar
  38. Russo J. E., Schoemaker P. J. H. (1989) Decision traps. Fireside, New YorkGoogle Scholar
  39. Saaty T. L. (1977) A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15: 234–281MATHCrossRefMathSciNetGoogle Scholar
  40. Saaty T. L. (2001) Decision making with dependence and feedback: The analytic network process. RWS Publications, PittsburghGoogle Scholar
  41. Saaty T. L., Ozdemir M. (2005) The Encyclicon. RWS Publications, PittsburghGoogle Scholar
  42. Saaty T. L., Shang J. S. (2007) Group decision-making: Head-count versus intensity of preference. Socio-Economic Planning Sciences 41(1): 22–37CrossRefGoogle Scholar
  43. Saaty T. L., Sodenkamp M. (2008) Making decisions in hierarchic and network systems. International Journal of Applied Decision Sciences 1(1): 24–79CrossRefGoogle Scholar
  44. Schoemaker P. J. H. (1993) Multiple scenario development: Its conceptual and behavioral foundation. Strategic Management Journal 14(3): 193–213CrossRefGoogle Scholar
  45. Schoemaker P. J. H., Russo J. E. (1993) A pyramid of decision approaches. California Management Review 36(1): 9–31Google Scholar
  46. Sevastjanov P., Figat P. (2007) Aggregation of aggregating modes in MCDM: Synthesis of Type 2 and Level 2 fuzzy sets. Omega 35: 505–523CrossRefGoogle Scholar
  47. Shannon C.E. (1948) A Mathematical theory of communication. Bell System Technical Journal 27: 379–423 623–656MATHMathSciNetGoogle Scholar
  48. Tavana M., Banerjee S. (1995) Strategic assessment model (SAM): A multiple criteria decision support system for evaluation of strategic alternatives. Decision Sciences 26: 119–143CrossRefGoogle Scholar
  49. Tavana, M., & Sodenkamp, M. A. (2009). A fuzzy multi-criteria decision analysis model for advanced technology assessment at the Kennedy Space Center. Journal of the Operational Research Society, doi:10.1057/jors.2009.107.
  50. Thomas H. (1985) Decision analysis and strategic management of research and development. Research & Development Management 15(1): 3–22Google Scholar
  51. Triantaphyllou E. (2000) Multi-criteria decision making methods: A comparative study. Kluwer, BostonMATHGoogle Scholar
  52. Triantaphyllou E., Mann S. H. (1995) Using the analytic hierarchy process for decision making in engineering applications: Some challenges. International Journal of Industrial Engineering: Applications and Practice 2(1): 35–44Google Scholar
  53. Uno, T. (2003). Efficient computation of power indices for weighted majority games, NII Technical Report, National Institute of Informatics.Google Scholar
  54. Vepsalainen A. P. J., Lauro G. L. (1988) Analysis of R&D portfolio strategies for contract competition. IEEE Transactions on Engineering Management 35(3): 181–186CrossRefGoogle Scholar
  55. Vickers B. (1992) Using GDSS to examine the future European automobile industry. Futures 24: 789–812CrossRefGoogle Scholar
  56. Wang J., Hwang W.-L. (2007) A fuzzy set approach for R&D portfolio selection using a real options valuation model. Omega 35: 247–257CrossRefGoogle Scholar
  57. Weigelt K., Macmillan I. (1988) An integrative strategic analysis framework. Strategic Management Journal 9: 27–40CrossRefGoogle Scholar
  58. Yang T., Hsieh C.-H. (2009) Six-Sigma project selection using national quality award criteria and the Delphi fuzzy multiple criteria decision-making method. Expert Systems with Applications 36(4): 7594–7603CrossRefGoogle Scholar
  59. Yeh C.-H., Chang Y.-H. (2009) Modeling subjective evaluation for fuzzy group multicriteria decision making. European Journal of Operational Research 194(2): 464–473MATHCrossRefGoogle Scholar
  60. Yoe, C. (2002). Trade-Off Analysis Planning and Procedures Guidebook. Prepared for Institute for Water Resources, U.S. Army Corps of Engineers.Google Scholar
  61. Zadeh L. A. (1965) Fuzzy sets. Information and Control 8: 338–353MATHCrossRefMathSciNetGoogle Scholar
  62. Zadeh L. A. (1971) Quantitative fuzzy semantics. Information Sciences 3: 177–200MATHCrossRefMathSciNetGoogle Scholar
  63. Zadeh L. A. (1996) Fuzzy logic and the calculi of fuzzy rules and fuzzy graphs. Multiple-Valued Logic 1: 1–38MATHMathSciNetGoogle Scholar
  64. Zeleny M. A. (1982) Multiple criteria decision making. McGraw-Hill, New YorkMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Madjid Tavana
    • 1
  • Mariya A. Sodenkamp
    • 2
  • Mohsen Pirdashti
    • 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 5Germany
  3. 3.Chemical Engineering Department, Faculty of EngineeringShomal UniversityAmolIran

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