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Mixed integer (0–1) fractional programming for decision support in paper production industry

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  • Claassen, G.D.H.

Abstract

This paper presents an effective and efficient method for solving a special class of mixed integer fractional programming (FP) problems. We take a classical reformulation approach for continuous FP as a starting point and extend it for solving a more general class of mixed integer (0–1) fractional programming problems.

Suggested Citation

  • Claassen, G.D.H., 2014. "Mixed integer (0–1) fractional programming for decision support in paper production industry," Omega, Elsevier, vol. 43(C), pages 21-29.
    • Handle: RePEc:eee:jomega:v:43:y:2014:i:c:p:21-29
    DOI: 10.1016/j.omega.2013.05.008
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    Cited by:

    1. József Mezei & Matteo Brunelli & Christer Carlsson, 2017. "A fuzzy approach to using expert knowledge for tuning paper machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 605-616, June.
    2. Ritu Arora & Kavita Gupta, 2018. "Branch and bound algorithm for discrete multi- level linear fractional programming problem," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(2), pages 5-21.

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