International Journal of Business Analytics and Intelligence

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On the Role of Fuzzy Sets and Systems in Managerial Decision Making


Affiliations
1 Department of Business Administration, Asper School of Business, University of Manitoba, Winnipeg, Canada
2 Department of Supply Chain Management, Asper School of Business, University of Manitoba, Winnipeg, Canada
     

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The present paper discusses the history and the logic behind the conception and development of fuzzy sets and systems and the fuzzy logic therein, and how its suitability was recognised in modeling the decision making problems in management science, finance, marketing, and supply chain management, among others. Lately, fuzzy models are being used in multiattribute decision making. Problems such as brand choice, consumer behaviour, head hunting in human resource management, in which human preferences are involved and have to be evaluated quantifiably to arrive at a tangible decision; fall in this category. The paper tries to explain the intricate fuzzy logic in a manager friendly language and how it is applied in the decision making process where linguistic ambiguity or imprecision is involved. We present the application of fuzzy logic and possibility theory in modeling and the solutions of business problems in various fields such as Finance, marketing, supply chain among others.

Keywords

Fuzzy Number, Fuzzy Mathematics and Fuzzy Logic, Possibility Theory, Α-Cuts, Fuzzy Equations, Fuzzy Decisions, Possibilistic Mean and Variance.
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  • Appadoo, S. S. (2006). Pricing financial derivatives with fuzzy algebraic models: A theoretical and computational approach, Ph.D. Thesis, University of Manitoba, Winnipeg, Manitoba, Canada.

  • Appadoo, S. S., Bector, C. R., & Bhatt, S. K. (2010). A fuzzy linear fractional programming model. Proceedings of the Administrative Sciences Association of Canada Conference, Regina, 22-25.

  • Appadoo, S. S., Bector, C. R., & Chandra, S. (2005). Binomial option pricing model using O(2,2)-Tr. T.F.N. Administrative Sciences Association of Canada Conference Proceedings, Toronto, Ointario.

  • Appadoo, S. S., Bhatt, S. K., & Bector, C. R. (2008). Application of possibility theory to investment decisions. Fuzzy Optimization and Decision Making, 7, 35-57.

  • Appadoo, S. S., Bhatt, S. K., Bector, C. R., & Chandra, S. (2007). Possibility supported TOPSIS model with application to vendor selection problem in supply chain management. Proceeding of the Administrative Sciences Association of Canada Conference, Ottawa, Canada. Recipients of the "honorable Mention Award" at the conference

  • Appadoo, S. S., Bhatt, S. K., & Bector, C. R., & Sharma, V. N. (2008). A possibility assisted fuzzy EOQ inventory model. Proceeding of the Administrative Sciences Association of Canada Conference, Dalhousie, Canada.

  • Appadoo, S. S., Bhatt, S. K., Bector, C. R., & Sharma, V. N. (2009)."(O, e) Mixed Strategy for Group Multi-Attribute TOPSIS model with Application to Supplier Selection Problem. Proceeding of the Administrative Sciences Association of Canada Conference Niagara Falls, Canada.

  • Bector, C. R., Bhatt, S. K., & Appadoo, S. S. (2003). Capital asset pricing model under fuzzy environment. ASAC Conference Proceedings, Halifax, Nova Scotia.

  • Bector, C. R., Bhatt, S. K., & Sharma, V.N. (2002). A finite Iteration Technique for a Fuzzy Quadratic Programming Problem" 7th International Symposium on Generalized Convexity/ Monotonicity, Hanoi, Aug. 27-31, (Also chapter 4, Bector and Chandra (2005)

  • Bector, C. R., & Chandra, S. (2005). Fuzzy mathematical Programming and Fuzzy matrix Games, Springer, Berlin.

  • Bector, C. R., & Chandra, S. (2002). On duality on linear programming under fuzzy environment. Fuzzy Sets and Systems, 125, 317-325.

  • Bector, C. R., Chandra, S., & Vidyottama, V. (2004). Matrix games with fuzzy goals and fuzzy linear programming duality. Fuzzy Optimization and Decision making, 3, 255-269.

  • Bellman, R. E., & Zadeh, L. A. Decision-making in a fuzzy environment. Management Science, 17(4), B141-B164.

  • Bhatt, S. K., Bhatnagar, N., & Appadoo, S. S. (2009). Presented at the Annual Meeting of the Society for Judgment and Decision Making, 21-23, Boston, MA.

  • Bojadziev, G., & Bojadziev, M. (1997). Fuzzy logic for business, finance and management. World Scientific, Singapore.

  • Carlsson, C., & Fuller, R. (2001). On possibilstic mean value and variance of fuzzy numbers. Fuzzy Sets and Systems, 122, 315-326.

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operations Research, 12(6), 429-444.

  • Dubois, D., & Prade, H. (1989). Fuzzy sets, probability and measurement. E. J. O. R., 40, 135-154.

  • Fuller, R., & Majlender, P. (2003).On weighted possibilistic mean and variance of fuzzy numbers. Fuzzy sets and systems, 136, 363-374.

  • Goetschel, R., & Voxman, W. (1986). Elementary fuzzy calculus. Fuzzy Sets and Systems, 18, 31-43.

  • Guo, T., & Bhatt, S. K. (1995). Fuzzy discrete multiattribute utility approach to project selection, Proceedings of Administrative Sciences Association of Canada, Windsor, Ontario, Canada.

  • Kaufmann, A., & Gupta, M. M. (1991). Introduction to fuzzy arithmetic, theory and applications, Van Nostrand Reinhold.

  • Klir, G. H., & Bo, Y. (1995). Fuzzy Sets and Fuzzy Logic- Theory and Applications, Prentice Hall, N.J.

  • Lertworasirikul, S., Fang, S., Joins, J. A., & Nuttle, H. L.W. (2003).Fuzzy data envelopment analysis: A possibility approach. Fuzzy Sets and Systems, 139, 379-394.

  • Long, L. D., & Ohsato, A.(2008). Fuzzy critical chain method for project scheduling underresource constraints and uncertainty. International Journal of Project management, 26, 688-698.

  • Lowen, R., & Verschoren, A. (2008). Foundation of genetic optimization, 2, Application of fuzzy control, Genetic algorithm and neural networks, Springer.

  • De Luca, A., & Termini, S. (1972). A definition of a non probabilistic entropy in the setting of fuzzy set theory. Information Control, 20, 301-312.

  • Markovitz, H. M. (1952). Portfolio selection. The Journal of Finance, 12, 77-91.

  • Markovitz, H. M. (1956). The optimization of quadratic function subject to linear constraints. Naval Research Logistic Quarterly, 3, 111-133.

  • Mehra, A., Chandra, S., & Bector, C. R. (2007). Acceptable optimality in linear fractional programming with fuzzy coefficients. Fuzzy Optimization & Decision Making, 6, 5-16.

  • Muzzioli, S., & Torriceli, C. (2004). A multi period binomial model for pricing options in a vague world. Journal of Economic Dynamics and Control, 28, 861-887.

  • Liu, S. T. (2009). A revisit to quadratic programming with fuzzy parameters. Chaos, Soliton, and Fractals, 41, 1401-1407.

  • Smimou, K., Bhatt, S. K., & Dahl, D. W. (2005). New product ideas screening decision with approximate evaluations: Optimization approach. Fuzzy Economic Review, 10(2), 45-60.

  • Sukawa, M., (1993). Fuzzy sets and Interactive Optimization, Plenum Press, N.Y.

  • Sutardi, C., Bector, C. R., & Ian, G. (1995). Multiobjective water resource planning under budgetary uncertainty and fuzzy environment. European Journal of Operational Research, 82(3), 556-591.

  • Thavaneswaran, A., Thiagarajah, K., & Appadoo, S. S. (2007). Fuzzy Coefficient Valatility (FCV) models with applications. Mathematical and Computer Modelling, 45, 777-786.

  • Verworn, B. (2009). SA structural equation model of the impact of the "fuzzy front end" on the success of new product development. Research Policy, 38, 1571-1581

  • Yuh-Wen, C. (2001). Data envelopment analysis with fuzzy inputs and outputs. IJQM, 7(3), 165-176.

  • Zadeh, L.A. (1965). Fuzzy Sets. Information Control, 8.

  • Zadeh, L. A. (1999). Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, 100 Supplement, 9-34, Reprinted from the same journal, 1, (1978), 3-28.

  • Zimmerman, H. J., (1983). Fuzzy mathematical Programming. Computers and Operations Research, 10(4), 291-298.


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  • On the Role of Fuzzy Sets and Systems in Managerial Decision Making

Abstract Views: 318  |  PDF Views: 2

Authors

C. R. Bector
Department of Business Administration, Asper School of Business, University of Manitoba, Winnipeg, Canada
S. K. Bhatt
Department of Supply Chain Management, Asper School of Business, University of Manitoba, Winnipeg, Canada
S. S. Appadoo
Department of Supply Chain Management, Asper School of Business, University of Manitoba, Winnipeg, Canada

Abstract


The present paper discusses the history and the logic behind the conception and development of fuzzy sets and systems and the fuzzy logic therein, and how its suitability was recognised in modeling the decision making problems in management science, finance, marketing, and supply chain management, among others. Lately, fuzzy models are being used in multiattribute decision making. Problems such as brand choice, consumer behaviour, head hunting in human resource management, in which human preferences are involved and have to be evaluated quantifiably to arrive at a tangible decision; fall in this category. The paper tries to explain the intricate fuzzy logic in a manager friendly language and how it is applied in the decision making process where linguistic ambiguity or imprecision is involved. We present the application of fuzzy logic and possibility theory in modeling and the solutions of business problems in various fields such as Finance, marketing, supply chain among others.

Keywords


Fuzzy Number, Fuzzy Mathematics and Fuzzy Logic, Possibility Theory, Α-Cuts, Fuzzy Equations, Fuzzy Decisions, Possibilistic Mean and Variance.

References