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Branch and bound computational method for multi-objective linear fractional optimization problem

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Abstract

Present research deals with more efficient solution of a multi-objective linear fractional (MOLF) optimization problem by using branch and bound method. The MOLF optimization problem is reduced into multi-objective optimization problem by a transformation. The reduced multi-objective optimization problem is converted into single objective optimization problem by giving suitable weight for each objective. The equivalency theorems are established. Weak duality concept is used to compute the bounds for each partition and some theoretical results are also established. The proposed method is motivated by the work of Shen et al. (J Comput Appl Math 223:145–158, 2009). Matlab code is designed for the proposed method to run all the simulated results and it is applied on two numerical problems. The efficiency of the method is measured by comparing with earlier established method.

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Acknowledgments

The authors would like to acknowledge many helpful comments and suggestions of anonymous reviewers of this paper to improve manuscript. First author is also very thankful to his fellow research scholars Ms. Garima Mishra and Mrs. Rati Shukla for their help in programming.

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Authors and Affiliations

  1. Department of Mathematics, Motilal Nehru National Institute of Technology, Allahabad, 211004, India

    Deepak Bhati & Pitam Singh

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  1. Deepak Bhati
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  2. Pitam Singh
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Correspondence to Pitam Singh.

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Bhati, D., Singh, P. Branch and bound computational method for multi-objective linear fractional optimization problem. Neural Comput & Applic 28, 3341–3351 (2017). https://doi.org/10.1007/s00521-016-2243-6

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  • DOI: https://doi.org/10.1007/s00521-016-2243-6

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