A new Approach for Obtaining Optimal Solution of Unbalanced Fuzzy Transportation Problem
DOI:
https://doi.org/10.24297/ijct.v15i6.3977Keywords:
Optimization, Unbalanced Fuzzy transportation problem, Roubast Ranking technique, Trapezoidal fuzzy number, optimal solution, Modified VAM, Total opportunity cost.Abstract
The present paper attempts to study the unbalanced fuzzy transportation problem so as to minimize the transportationcost of products when supply, demand and cost of the products are represented by fuzzy numbers. In this paper, authors
use Roubast ranking technique to transform trapezoidal fuzzy numbers to crisp numbers and propose a new algorithm to
find the fuzzy optimal solution of unbalanced fuzzy transportation problem. The proposed algorithm is more efficient than
other existing algorithms like simple VAM and is illustrated via numerical example. Also, a comparison between the results
of the new algorithm and the result of algorithm using simple VAM is provided.
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References
I. Zadeh, L. A.. 1965. Fuzzy sets. Information and control, 8, 338-353.
II. Chanas, S., Kuchta, D. 1996.A concept of the optimal solution of the transportation problem with fuzzy cost
coefficients. Fuzzy Sets and Systems 82, 299-305.
III. Liu, Shiang-Tai., Chiang, Kao. 2004. Solving fuzzy transportation problems based on extension principle, European
Journal of Operational Research, 153, 661–674.
IV. Saad, O. M., Abbas, S. A. 2003. A parametric study on transportation problem under fuzzy environment. The Journal
of Fuzzy Mathematics, 115-124.
V. Gani, A., Razak, K. A. 2006. Two stage fuzzy transportation problem. Journal of physical sciences, 63-69.
VI. Hitchcock, F. L. 1941. The distribution of a product from several sources to numerous localities. Journal of Math.
Phys. 20, 224-230.
VII. Dantzig, G. B. 1963.Linear Programming and Extensions. Princeton University Press, Princeton.
VIII. Chen, S. H. 1985. Operations on fuzzy numbers with function principle. Tamkang Journal of Management Sciences,
6, 13-25.
IX. Riggs, J. L., Inoue, M. S. 1975. Introduction to Operation Research and Management Science. McGraw-Hill, New
York.
X. Joshi, N., Chauhan, S. S. 2013. Solution of Fuzzy Transportation Problem using Improved VAM with Roubast
Ranking Technique. International Journal of Computer Applications 82(15), 6-8.
XI. Saini, R. K., Sanghal, A., and Prakash, Om. 2015. Unbalanced problems in fuzzy environment using centroid ranking
technique. International Journal of Computer Applications, 110(11), 27-33.
XII. Rani, D., Gulati, T. R., and Kumar, A. 2014. A method for unbalanced transportation problems in fuzzy environment.
Sadhana, 39(3), 573-581.
XIII. Edward Samuel, A., and Venkatachalapathy, M. 2013. IZPM for unbalanced fuzzy transportation problems.
International Journal of Pure and Applied Mathematics, 86(4), 689-700.
XIV. Kadhirvel, K., Balamurugan, K. 2013. Method for solving unbalanced transportation problems using trapezoidal fuzzy
numbers. IJERA, 3(4), 2591-2596.
XV. Edward Samuel, A., Venkatachalapathy, M. 2012. A new dual based approach for the unbalanced fuzzy
transportation problem. Applied Mathematical Sciences, 6(89), 4443- 4455.
XVI. Sobha, K. R. 2014. Profit maximization of unbalanced fuzzy transportation problem. International Journal of Science
and Research, 3(11), 2300-2302.
XVII. Kumar, A., Kaur, A. 2011. Application of linear programming for solving fuzzy transportation problems. J. Appl. Math
and Informatics, 29(3-4), 831-846.
XVIII. Kumar, A., Kaur, A. 2012. Methods for solving unbalanced fuzzy transportation problems. Operational Research,
12(3), 287-316.
XIX. Kaur, A., Kumar, A. 2011. A new method for solving fuzzy transportation problems using ranking function. Applied
Mathematical Modelling, 35(12), 5652-5661.
XX. Pandian, P., Natarajan, G. 2010. A new algorithm for finding a fuzzy optimal solution for fuzzy transportation
problems. Applied Mathematical Sciences, 4, 79-90.
II. Chanas, S., Kuchta, D. 1996.A concept of the optimal solution of the transportation problem with fuzzy cost
coefficients. Fuzzy Sets and Systems 82, 299-305.
III. Liu, Shiang-Tai., Chiang, Kao. 2004. Solving fuzzy transportation problems based on extension principle, European
Journal of Operational Research, 153, 661–674.
IV. Saad, O. M., Abbas, S. A. 2003. A parametric study on transportation problem under fuzzy environment. The Journal
of Fuzzy Mathematics, 115-124.
V. Gani, A., Razak, K. A. 2006. Two stage fuzzy transportation problem. Journal of physical sciences, 63-69.
VI. Hitchcock, F. L. 1941. The distribution of a product from several sources to numerous localities. Journal of Math.
Phys. 20, 224-230.
VII. Dantzig, G. B. 1963.Linear Programming and Extensions. Princeton University Press, Princeton.
VIII. Chen, S. H. 1985. Operations on fuzzy numbers with function principle. Tamkang Journal of Management Sciences,
6, 13-25.
IX. Riggs, J. L., Inoue, M. S. 1975. Introduction to Operation Research and Management Science. McGraw-Hill, New
York.
X. Joshi, N., Chauhan, S. S. 2013. Solution of Fuzzy Transportation Problem using Improved VAM with Roubast
Ranking Technique. International Journal of Computer Applications 82(15), 6-8.
XI. Saini, R. K., Sanghal, A., and Prakash, Om. 2015. Unbalanced problems in fuzzy environment using centroid ranking
technique. International Journal of Computer Applications, 110(11), 27-33.
XII. Rani, D., Gulati, T. R., and Kumar, A. 2014. A method for unbalanced transportation problems in fuzzy environment.
Sadhana, 39(3), 573-581.
XIII. Edward Samuel, A., and Venkatachalapathy, M. 2013. IZPM for unbalanced fuzzy transportation problems.
International Journal of Pure and Applied Mathematics, 86(4), 689-700.
XIV. Kadhirvel, K., Balamurugan, K. 2013. Method for solving unbalanced transportation problems using trapezoidal fuzzy
numbers. IJERA, 3(4), 2591-2596.
XV. Edward Samuel, A., Venkatachalapathy, M. 2012. A new dual based approach for the unbalanced fuzzy
transportation problem. Applied Mathematical Sciences, 6(89), 4443- 4455.
XVI. Sobha, K. R. 2014. Profit maximization of unbalanced fuzzy transportation problem. International Journal of Science
and Research, 3(11), 2300-2302.
XVII. Kumar, A., Kaur, A. 2011. Application of linear programming for solving fuzzy transportation problems. J. Appl. Math
and Informatics, 29(3-4), 831-846.
XVIII. Kumar, A., Kaur, A. 2012. Methods for solving unbalanced fuzzy transportation problems. Operational Research,
12(3), 287-316.
XIX. Kaur, A., Kumar, A. 2011. A new method for solving fuzzy transportation problems using ranking function. Applied
Mathematical Modelling, 35(12), 5652-5661.
XX. Pandian, P., Natarajan, G. 2010. A new algorithm for finding a fuzzy optimal solution for fuzzy transportation
problems. Applied Mathematical Sciences, 4, 79-90.
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Published
2016-04-20
How to Cite
Joshi, N., & Chauhan, S. S. (2016). A new Approach for Obtaining Optimal Solution of Unbalanced Fuzzy Transportation Problem. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(6), 6824–6832. https://doi.org/10.24297/ijct.v15i6.3977
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Research Articles