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Erschienen in: Neural Computing and Applications 11/2017

07.03.2016 | Original Article

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

verfasst von: Deepak Bhati, Pitam Singh

Erschienen in: Neural Computing and Applications | Ausgabe 11/2017

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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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Metadaten
Titel
Branch and bound computational method for multi-objective linear fractional optimization problem
verfasst von
Deepak Bhati
Pitam Singh
Publikationsdatum
07.03.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2243-6

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