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An Enhanced Algorithm for Solving Fuzzy Transportation Problems Using Modified SVAM

Issue Abstract

Abstract: 

            The fuzzy transportation problem constitutes a specialized class of fuzzy linear programming problems (LPPs), which is designed to minimize total fuzzy transportation cost, and satisfy the constraints of both supply and demand. The fuzzy optimization models incorporate fuzzy constraints and objectives, enabling decision-makers to make more robust and flexible transportation plans. As a fundamental optimization model in logistics and operations research, it seeks an optimal distribution strategy that ensures all supply sources as well as demands meet requirements at the lowest possible cost through finding an initial basic feasible solution (IBFS). This paper puts forward a new transportation method called (MSVAM). The unique contribution of the new proposed method lies in minimizing the total transportation cost compared to other existing methods, while reducing the time and effort of the computational process. To validate the effectiveness and applicability of the proposed method, a number of examples (one trapezoidal and the other hexagonal) are solved using MSVAM, and compared with other contemporary methods.

Keywords: Transportation Problem, FTP, IBFS, VAM, SVAM, Robust Ranking Method, MSVAM


Author Information
Najlaa Abdulwahed Ali Qasem Almansoury, Research Scholar,; Satakshi, Assistant Professor, Department of Mathematics & Statistics, Sam Higginbottom University of Agriculture, Technology and Sciences (SHUATS), Prayagraj, UP, India-211008.
Issue No
8
Volume No
11
Issue Publish Date
05 Aug 2025
Issue Pages
1-17

Issue References

REFERENCES

  1. Aroniadi, C., & Beligiannis, G. N. (2024). Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach. Applied Sciences14(13), 5885.

  2. Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B-141–B-164. https://doi.org/10.1287/mnsc.17.4.B141 

  3. Chanas, S., Kołodziejczyk, W., & Machaj, A. (1984). A fuzzy approach to the transportation problem. Fuzzy sets and Systems13(3), 211-221. https://doi.org/10.1016/0165-0114(84)90057-5 

  4. Chen, S.-J., & Hwang, C.-L. (1992). Fuzzy multiple attribute decision making: Methods and applications. Springer. https://doi.org/10.1007/978-3-642-46768-4 

  5. Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9(6), 613–626. https://doi.org/10.1080/00207727808941724

  6. Ebrahimnejad, A. (2016). A new method for solving fully fuzzy transportation problems. Journal of Intelligent & Fuzzy Systems, 30(1), 185–194. https://doi.org/10.3233/IFS-151865

  7. Elumalai, P., Prabu, K., & Santhoshkumar, S. (2017). Fuzzy transportation problem using hexagonal fuzzy numbers by robust ranking method. Emperor Int. J. Finance. Manag. Res. UGC Jr45308, 52-58.

  8. Kaufmann, A., & Gupta, M. M. (1985). Introduction to fuzzy arithmetic: Theory and applications. Van Nostrand Reinhold.

  9. Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. Amsterdam, The Netherlands: Elsevier.

  10. Kaur, A., & Kumar, A. (2012). A new approach for solving fuzzy transportation problems using generalized trapezoidal fuzzy numbers. Applied soft computing12(3), 1201-1213.

  11. Liu, B., & Kao, J. (2004). Solving fuzzy transportation problems based on extension principle. European Journal of Operational Research, 153(3), 661–674. https://doi.org/10.1016/S0377-2217(02)00731-2 

  12. Moore, R. E., & Yang, C. T. (1959). Interval analysis I. Technical Document LMSD-285875, Lockheed Missiles and Space Division, Sunnyvale, CA, USA.

  13. Pandian, P., & Natarajan, G. (2010). A new algorithm for finding an optimal solution for fuzzy transportation problems. Applied Mathematical Sciences, 4(2), 79–90.

  14. Satakshi, & Henry, V. V. (2024). A novel approach to find simple, pragmatic solutions to transportation problems. International Journal of Mathematics in Operational Research27(4), 496-508.

  15. Sharma, D., Bisht, D. C., & Srivastava, P. K. (2024). Solution of fuzzy transportation problem based upon pentagonal and hexagonal fuzzy numbers. International Journal of System Assurance Engineering and Management15(9), 4348-4354.

  16. Solaiappan, S., & Jeyaraman, K. (2014). A new optimal solution method for trapezoidal fuzzy transportation problem. International journal of advanced research2(1), 933-942.

  17. Wang, Y.-M., & Elhag, T. M. S. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Systems with Applications, 31(2), 309–319. https://doi.org/10.1016/j.eswa.2005.09.040

  18. Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143–161. https://doi.org/10.1016/0020-0255(81)90017-5

  19. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X 

  20. Zimmermann, H.-J. (1991). Fuzzy set theory—and its applications (2nd ed.). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-7949-7