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Article

An Improved Vogel's Approximation Method for the Transportation Problem

by
Serdar Korukoğlu
1,* and
Serkan Ballı
2,*
1
Department of Computer Engineering, Ege University, 35000, Bornova, Đzmir, Turkey
2
Department of Statistics, Muğla University, 48187, Muğla, Turkey
*
Authors to whom correspondence should be addressed.
Math. Comput. Appl. 2011, 16(2), 370-381; https://doi.org/10.3390/mca16020370
Submission received: 1 August 2011 / Accepted: 1 August 2011 / Published: 1 August 2011

Abstract

Determining efficient solutions for large scale transportation problems is an important task in operations research. In this study, Vogel’s Approximation Method (VAM) which is one of well-known transportation methods in the literature was investigated to obtain more efficient initial solutions. A variant of VAM was proposed by using total opportunity cost and regarding alternative allocation costs. Computational experiments were carried out to evaluate VAM and improved version of VAM (IVAM). It was seen that IVAM conspicuously obtains more efficient initial solutions for large scale transportation problems. Performance of IVAM over VAM was discussed in terms of iteration numbers and CPU times required to reach the optimal solutions.
Keywords: Transportation Problem; Integer Programming; Vogel’s Approximation Method; Total Opportunity Cost; Simulation Experiments Transportation Problem; Integer Programming; Vogel’s Approximation Method; Total Opportunity Cost; Simulation Experiments

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MDPI and ACS Style

Korukoğlu, S.; Ballı, S. An Improved Vogel's Approximation Method for the Transportation Problem. Math. Comput. Appl. 2011, 16, 370-381. https://doi.org/10.3390/mca16020370

AMA Style

Korukoğlu S, Ballı S. An Improved Vogel's Approximation Method for the Transportation Problem. Mathematical and Computational Applications. 2011; 16(2):370-381. https://doi.org/10.3390/mca16020370

Chicago/Turabian Style

Korukoğlu, Serdar, and Serkan Ballı. 2011. "An Improved Vogel's Approximation Method for the Transportation Problem" Mathematical and Computational Applications 16, no. 2: 370-381. https://doi.org/10.3390/mca16020370

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