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An Effective Branch and Bound Algorithm for Minimax Linear Fractional Programming

Author

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  • Hong-Wei Jiao
  • Feng-Hui Wang
  • Yong-Qiang Chen

Abstract

An effective branch and bound algorithm is proposed for globally solving minimax linear fractional programming problem (MLFP). In this algorithm, the lower bounds are computed during the branch and bound search by solving a sequence of linear relaxation programming problems (LRP) of the problem (MLFP), which can be derived by using a new linear relaxation bounding technique, and which can be effectively solved by the simplex method. The proposed branch and bound algorithm is convergent to the global optimal solution of the problem (MLFP) through the successive refinement of the feasible region and solutions of a series of the LRP. Numerical results for several test problems are reported to show the feasibility and effectiveness of the proposed algorithm.

Suggested Citation

  • Hong-Wei Jiao & Feng-Hui Wang & Yong-Qiang Chen, 2014. "An Effective Branch and Bound Algorithm for Minimax Linear Fractional Programming," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:160262
    DOI: 10.1155/2014/160262
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    References listed on IDEAS

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    2. Goedhart, Marc H. & Spronk, Jaap, 1995. "Financial planning with fractional goals," European Journal of Operational Research, Elsevier, vol. 82(1), pages 111-124, April.
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