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On flexible product-mix decision problems under randomness and fuzziness

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  • Hasuike, Takashi
  • Ishii, Hiroaki
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    Abstract

    This paper considers several models of product-mix decision problems and production planning problems under uncertain conditions, and shows that these are extensional and versatile models for resolving previous product-mix problems. These proposed models include randomness derived from statistical analysis based on historical data, ambiguity of decision maker's intuition and the quality of received information, and flexibility in accomplishing the original plan. Furthermore, given that the upper limit values of some constraints have flexibility, and given a decision maker's level of satisfaction, we propose a flexible product mix of problems using the theory of constraints (TOC), and develop an efficient solution method. We then provide a numerical example that compares our models with some previous basic models. Efficiency of flexibility is obtained when our proposed models are applied to several conditions, such as measurable changes from the expected value of future returns.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 37 (2009)
    Issue (Month): 4 (August)
    Pages: 770-787

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    Handle: RePEc:eee:jomega:v:37:y:2009:i:4:p:770-787

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    Keywords: Flexible product-mix decision problem Stochastic and fuzzy programming Theory of constraints Level of satisfaction of decision maker;

    References

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    1. Werner Dinkelbach, 1967. "On Nonlinear Fractional Programming," Management Science, INFORMS, vol. 13(7), pages 492-498, March.
    2. Letmathe, Peter & Balakrishnan, Nagraj, 2005. "Environmental considerations on the optimal product mix," European Journal of Operational Research, Elsevier, vol. 167(2), pages 398-412, December.
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    4. Bhattacharya, Arijit & Vasant, Pandian, 2007. "Soft-sensing of level of satisfaction in TOC product-mix decision heuristic using robust fuzzy-LP," European Journal of Operational Research, Elsevier, vol. 177(1), pages 55-70, February.
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    8. Olhager, Jan, 1993. "Manufacturing flexibility and profitability," International Journal of Production Economics, Elsevier, vol. 30(1), pages 67-78, July.
    9. Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
    10. Gong, Zhejun & Hu, Sun, 2008. "An economic evaluation model of product mix flexibility," Omega, Elsevier, vol. 36(5), pages 852-864, October.
    11. Lau, Hon-Shiang & Lau, Amy Hing-Ling, 1999. "Manufacturer's pricing strategy and return policy for a single-period commodity," European Journal of Operational Research, Elsevier, vol. 116(2), pages 291-304, July.
    12. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
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    Cited by:
    1. Wellington, John F. & Guiffrida, Alfred L. & Lewis, Stephen A., 2014. "Interior analysis of the green product mix solution," European Journal of Operational Research, Elsevier, vol. 237(3), pages 966-974.
    2. Georgiadis, Michael C. & Tsiakis, Panagiotis & Longinidis, Pantelis & Sofioglou, Maria K., 2011. "Optimal design of supply chain networks under uncertain transient demand variations," Omega, Elsevier, vol. 39(3), pages 254-272, June.
    3. Seebacher, Gottfried & Winkler, Herwig, 2014. "Evaluating flexibility in discrete manufacturing based on performance and efficiency," International Journal of Production Economics, Elsevier, vol. 153(C), pages 340-351.
    4. Martzoukos, Spiros H. & Zacharias, Eleftherios, 2013. "Real option games with R&D and learning spillovers," Omega, Elsevier, vol. 41(2), pages 236-249.

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