Document Type : Review Paper

Authors

1 Faculty of Economics and Management, University of Social Sciences and Management of Bamako (USSGB), Quartier du Fleuve Rue 310, Porte 238, Mali.

2 Department of Applied Mathematics (FSEG), Université des Sciences Sociales et de Gestion de Bamako (USSGB), Quartier du Fleuve Rue 310, Porte 238, Mali.

Abstract

Transportation problem is an important network structured linear programming problem that arises in several contexts and has deservedly received a great deal of attention in the literature. The central concept in this problem is to find the least total transportation cost of a commodity in order to satisfy demands at destinations using available supplies at origins in a crisp environment. In real life situations, the decision maker may not be sure about the precise values of the coefficients belonging to the transportation problem. The aim of this paper is to introduce a formulation of fully fuzzy transportation problem involving trapezoidal fuzzy numbers for the transportation costs and values of supplies and demands. We propose a two-step method for solving fuzzy transportation problem where all of the parameters are represented by triangular fuzzy numbers i.e. two interval transportation problems. Since the proposed approach is based on classical approach it is very easy to understand and to apply on real life transportation problems for the decision makers. To illustrate the proposed approach four application examples are solved. The results show that the proposed method is simpler and computationally more efficient than existing methods in the literature.

Keywords

Main Subjects

  1. Kaur, J., & Kumar, A. (2016). An introduction to fuzzy linear programming problems Theory, Methods and Applications. Springer.
  2. Nasseri, S. H., Ebrahimnejad, A., & Cao, B. Y. (2019). Fuzzy linear programming. In Fuzzy linear programming: solution techniques and applications(pp. 39-61). Springer, Cham.
  3. Kaur, A., Kacprzyk, J., Kumar, A., & Analyst, C. F. (2020). Fuzzy transportation and transshipment problems. Springer International Publishing.
  4. Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems1(1), 45-55.
  5. ÓhÉigeartaigh, M. (1982). A fuzzy transportation algorithm. Fuzzy sets and systems8(3), 235-243.
  6. Chanas, S., Kołodziejczyk, W., & Machaj, A. (1984). A fuzzy approach to the transportation problem. Fuzzy sets and systems13(3), 211-221.
  7. Chanas, S., Delgado, M., Verdegay, J. L., & Vila, M. A. (1993). Interval and fuzzy extensions of classical transportation problems. Transportation Planning and Technology17(2), 203-218.
  8. Kuchta, D. (1996). A concept of the optimal solution of the transportation problem with fuzzy cost coefficients. Fuzzy sets and systems, 82(299), 299-305.
  9. Jiménez, F., & Verdegay, J. L. (1998). Uncertain solid transportation problems. Fuzzy sets and systems100(1-3), 45-57.
  10. Jiménez, F., & Verdegay, J. L. (1999). Solving fuzzy solid transportation problems by an evolutionary algorithm based parametric approach. European journal of operational research117(3), 485-510.
  11. Liu, S. T., & Kao, C. (2004). Solving fuzzy transportation problems based on extension principle. European journal of operational research153(3), 661-674.
  12. Gani, A. N., & Razak, K. A. (2006). Two stage fuzzy transportation problem. J. Phys. Sci. 10, 63–69. http://inet.vidyasagar.ac.in:8080/jspui/handle/123456789/720
  13. Li, L., Huang, Z., Da, Q., & Hu, J. (2008, May). A new method based on goal programming for solving transportation problem with fuzzy cost. 2008 international symposiums on information processing(pp. 3-8). IEEE.
  14. Lin, F. T. (2009, August). Solving the transportation problem with fuzzy coefficients using genetic algorithms. 2009 IEEE international conference on fuzzy systems(pp. 1468-1473). IEEE.
  15. Dinagar, D. S., & Palanivel, K. (2009). The transportation problem in fuzzy environment. International journal of algorithms, computing and mathematics2(3), 65-71.
  16. Pandian, P., & Natarajan, G. (2010). A new algorithm for finding a fuzzy optimal solution for fuzzy transportation problems. Applied mathematical sciences4(2), 79-90.
  17. Kumar, A., & Kaur, A. (2011). Application of linear programming for solving fuzzy transportation problems. Journal of applied mathematics & informatics29(3_4), 831-846.
  18. Gupta, A., Kumar, A., & Kaur, A. (2012). Mehar’s method to find exact fuzzy optimal solution of unbalanced fully fuzzy multi-objective transportation problems. Optimization letters6(8), 1737-1751.
  19. Ebrahimnejad, A. (2015). A duality approach for solving bounded linear programming problems with fuzzy variables based on ranking functions and its application in bounded transportation problems. International journal of systems science46(11), 2048-2060.
  20. Shanmugasundari, M., & Ganesan, K. (2013). A novel approach for the fuzzy optimal solution of fuzzy transportation problem. Transportation3(1), 1416-1424.
  21. Chandran, S., & Kandaswamy, G. (2016). A fuzzy approach to transport optimization problem. Optimization and engineering17(4), 965-980.
  22. Ebrahimnejad, A. (2016). Note on “A fuzzy approach to transport optimization problem”. Optimization and engineering17(4), 981-985.
  23. Kumar, A., & Kaur, A. (2014). Optimal way of selecting cities and conveyances for supplying coal in uncertain environment. Sadhana39(1), 165-187.
  24. Ebrahimnejad, A. (2015). An improved approach for solving fuzzy transportation problem with triangular fuzzy numbers. Journal of intelligent & fuzzy systems29(2), 963-974.
  25. Kaur, A., & Kumar, A. (2011). A new method for solving fuzzy transportation problems using ranking function. Applied mathematical modelling35(12), 5652-5661.
  26. Kaur, A., & Kumar, A. (2012). A new approach for solving fuzzy transportation problems using generalized trapezoidal fuzzy numbers. Applied soft computing12(3), 1201-1213.
  27. Ebrahimnejad, A. (2014). A simplified new approach for solving fuzzy transportation problems with generalized trapezoidal fuzzy numbers. Applied soft computing19, 171-176.
  28. Ramesh, G., & Ganesan, K. (2012). Duality theory for interval linear programming problems. IOSR journal of mathematics4(4), 39-47.
  29. Bisht, D. C., & Srivastava, P. K. (2019). One point conventional Model to optimize Trapezoidal Fuzzy Transportation problem. International journal of mathematical, engineering and management sciences4(5), 1251-1263.
  30. Kane, L., Sidibe, H., Kane, S., Bado, H., Konate, M., Diawara, D., & Diabate, L. (2021). A simplified new approach for solving fully fuzzy transportation problems with involving triangular fuzzy numbers. Journal of fuzzy extension and applications2(1), 89-105.