Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 160262, 8 pages.

An Effective Branch and Bound Algorithm for Minimax Linear Fractional Programming

Hong-Wei Jiao, Feng-Hui Wang, and Yong-Qiang Chen

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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.

Article information

Source
J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 160262, 8 pages.

Dates
First available in Project Euclid: 27 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425050730

Digital Object Identifier
doi:10.1155/2014/160262

Mathematical Reviews number (MathSciNet)
MR3226298

Citation

Jiao, Hong-Wei; Wang, Feng-Hui; Chen, Yong-Qiang. An Effective Branch and Bound Algorithm for Minimax Linear Fractional Programming. J. Appl. Math. 2014, Special Issue (2014), Article ID 160262, 8 pages. doi:10.1155/2014/160262. https://projecteuclid.org/euclid.jam/1425050730


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