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A Parametric Method for Solving the Linear Fractional Programming Problem

Author

Listed:
  • Hartmut Wolf

    (Institut fur Unternehmensforschung und Wirtschaftsmathematik, Fernuniversitat, Hagen, Germany)

Abstract

This paper describes an approach for determining the optimal solution of the linear fractional programming problem. This approach is based mainly on a parametric analysis of a related linear substitution problem. Whereas existing algorithms for linear fractional programs concentrate solely on determining the optimal solution, the approach described in this paper provides the decision maker with additional insights and valuable information on the underlying decision problem. When the usual algorithms are applied, this additional information, which is gained by the parametric analysis of the substitution problem, can be obtained only by further computational effort.

Suggested Citation

  • Hartmut Wolf, 1985. "A Parametric Method for Solving the Linear Fractional Programming Problem," Operations Research, INFORMS, vol. 33(4), pages 835-841, August.
    • Handle: RePEc:inm:oropre:v:33:y:1985:i:4:p:835-841
    DOI: 10.1287/opre.33.4.835
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    Cited by:

    1. Tunjo Perić & Josip Matejaš & Zoran Babić, 2023. "Advantages, sensitivity and application efficiency of the new iterative method to solve multi-objective linear fractional programming problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(3), pages 751-767, September.
    2. Chang, Ching-Ter, 2002. "On the posynomial fractional programming problems," European Journal of Operational Research, Elsevier, vol. 143(1), pages 42-52, November.
    3. Chang, Ching-Ter, 2006. "Formulating the mixed integer fractional posynomial programming," European Journal of Operational Research, Elsevier, vol. 173(2), pages 370-386, September.

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