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New Product Selection Using Fuzzy Linear Programming and Fuzzy Monte Carlo Simulation

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Advances in Computational Intelligence (IPMU 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 300))

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Abstract

Investment decisions are important due to their critical role in organizations’ success. Sometimes, especially in uncertain conditions the results obtained from traditional analysis techniques can be different from the real world results. Due to this fact the techniques that take uncertainty into account are preferred in investment analysis to aware of the effect of an uncertain environment. In this paper, fuzzy Monte Carlo simulation method is used to determine the best investment strategy on new product selection for an organization in the condition when the fuzzy net present value is not the only point of concern for decision making.

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References

  1. Chiu, C.Y., Park, C.S.: Fuzzy cash flow analysis using present worth criterion. The Engineering Economist 39(2), 113–138 (1994)

    Article  Google Scholar 

  2. Chen, S.J., Hwang, C.L., Hwang, F.P.: Fuzzy multiple attribute decision making: Methods and applications. Springer, Berlin (1992)

    Book  MATH  Google Scholar 

  3. Dubois, D., Prade, H.: Operations on fuzzy numbers. International Journal of Systems Science 9, 613–626 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  4. Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic_Theory and Applications. Prentice Hall PTR (1995)

    Google Scholar 

  5. Hanns, M.: Applied Fuzzy Arithmetics_An Introduction with Engineering Applications. Springer, Netherlands (2005)

    Google Scholar 

  6. Buckley, J.J., Lowers, L.J.: Monte Carlo Methods in Fuzzy Optimization. Springer, Berlin (2008)

    MATH  Google Scholar 

  7. Canada, J.R., White, J.A.: Capital investment decision analysis for management and engineering. Prentice-Hall, USA (1980)

    Google Scholar 

  8. Ross, T.J.: Fuzzy Logic With Engineering Applications. Mc-Graw Hill, USA (1995)

    MATH  Google Scholar 

  9. Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  10. Liao, C.N.: Fuzzy analytical hierarchy process and multi-segment goal programming applied to new product segmented under price strategy. Computers & Industrial Engineering 6, 831–841 (2011)

    Article  Google Scholar 

  11. Wei, C.C., Chang, H.W.: A new approach for selecting portfolio of new product development projects. Expert Systems with Applications 38, 429–434 (2011)

    Article  Google Scholar 

  12. Wang, Y.M., Chin, K.S.: A linear goal programming priority method for fuzzy analytic hierarchy process and its applications in new product screening. International Journal of Approximate Reasoning 49, 451–465 (2008)

    Article  MATH  Google Scholar 

  13. Ngan, S.C.: Decision making with extended fuzzy linguistic computing, with applications to new product development and survey analysis. Expert Systems with Applications 38, 14052–14059 (2011)

    Google Scholar 

  14. Ng, S.T., Xie, J., Skitmore, M., Cheung, Y.K.: A fuzzy simulation model for evaluating the concession items of public–private partnership schemes. Automation in Construction 17, 22–29 (2007)

    Article  Google Scholar 

  15. Prasad, M.H., Gaikwad, A.J., Srividya, A., Verma, A.K.: Failure probability evaluation of passive system using fuzzy Monte Carlo simulation. Nuclear Engineering and Design 241, 1864–1872 (2011)

    Article  Google Scholar 

  16. Wu, H.C.: Evaluate Fuzzy Riemann Integrals Using the Monte Carlo Method. Journal of Mathematical Analysis and Applications 264, 324–343 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  17. Park, H.J., Um, J.G., Woo, I., Kim, J.W.: Application of fuzzy set theory to evaluate the probability of failure in rock slopes. Engineering Geology (2011) (in press)

    Google Scholar 

  18. Bai, X., Asgarpoor, S.: Fuzzy-based approaches to substation reliability evaluation. Electric Power Systems Research 69, 197–204 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Industrial Engineering Department, İstanbul Technical University, Maçka, 34367, İstanbul, Turkey

    İrem Uçal Sarı & Cengiz Kahraman

Authors
  1. İrem Uçal Sarı
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  2. Cengiz Kahraman
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco  & Benedetto Matarazzo  & 

  2. CNRS UMR 7606, DAPA, LIP6 8, Université Pierre et Marie Curie - Paris6, rue du Capitaine Scott, F-75015, Paris, France

    Bernadette Bouchon-Meunier

  3. Dip. Matematica e Informatica, Università di Perugia, 06123, Perugia, Italy

    Giulianella Coletti

  4. Department of Computer and Management Science, University of Trento, Via Inama 5, 38122, Trento, Italy

    Mario Fedrizzi

  5. Machine Intelligence Institute — IONA College,, 10801, New Rochelle, NY, USA

    Ronald R. Yager

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© 2012 Springer-Verlag Berlin Heidelberg

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Uçal Sarı, İ., Kahraman, C. (2012). New Product Selection Using Fuzzy Linear Programming and Fuzzy Monte Carlo Simulation. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_46

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  • DOI: https://doi.org/10.1007/978-3-642-31724-8_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31723-1

  • Online ISBN: 978-3-642-31724-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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