Skip to content
2000
Volume 1, Issue 2
  • ISSN: 2666-2949
  • E-ISSN: 2666-2957

Abstract

Transport models have wide application areas in the real world and play an important role in reducing transportation costs, increasing service quality, . These models may have uncertain transportation costs and supply or demand capacities of the product. Hence, it would be effective to model the vagueness of customer demands, economic conditions, and technical or non-technical uncertainties because of uncontrollable factors. Therefore, we focus on developing a mathematical solution approach to the fuzzy transportation problems.

In this paper, an integrated approach is proposed for the solution of the fuzzy linear transportation problem that has fuzzy cost coefficients in the objective function. Since transportation problem is encountered frequently in the national and international environment, it is considered that proposing a new solution method to this problem will be useful.

Fuzzy cost coefficients are taken as trapezoidal fuzzy numbers due to their widespread use in the literature. Firstly, the fuzziness is removed by converting the original single objective fuzzy transportation problem into a crisp Multi-Objective Linear Programming Problem (MOLPP). After the classical payoff matrix is constructed, ratio matrices are obtained to scale the objectives. Then, an approach based on game theory is implemented to solve the MOLPP, which is handled as a zero-sum game.

Creating different ratio matrices in the game theory part of the approach can generate compromise solutions for the decision-makers. To demonstrate the effectiveness of the proposed approach, two numerical examples from the literature are solved. While the same solution is obtained in one of the examples, a different compromise solution set is generated, which could be presented to the decision-maker in the other example.

In this paper, we developed a novel game theory-based approach to the fuzzy transportation problem. The proposed approach overcomes the non-linear structure due to the uncertainty in the cost coefficients. The greatest advantage of the proposed approach is that it can generate more than one optimal solution for the decision-maker.

Loading

Article metrics loading...

/content/journals/flme/10.2174/2666294901666220324121629
2022-09-01
2024-11-26
Loading full text...

Full text loading...

References

  1. ChanasS. KołodziejczykW. MachajA. A fuzzy approach to the transportation problem.Fuzzy Sets Syst.198413321122110.1016/0165‑0114(84)90057‑5
    [Google Scholar]
  2. LiuS.T. KaoC. Solving fuzzy transportation problems based on extension principle.Eur. J. Oper. Res.2004153366167410.1016/S0377‑2217(02)00731‑2
    [Google Scholar]
  3. BasirzadehH. An approach for solving fuzzy transportation problem.Appl. Math. Sci.201153215491566
    [Google Scholar]
  4. BasirzadehH. AbbasiR. A new approach for ranking fuzzy numbers based on a-CutsJ. Appl. Math. inform.vol. 26, no. 3_4, pp. 767-778, 2008.
    [Google Scholar]
  5. KaurA. KumarA. A new method for solving fuzzy transportation problems using ranking function.Appl. Math. Model.201135125652566110.1016/j.apm.2011.05.012
    [Google Scholar]
  6. OjhaA. MondalS.K. MaitiM. Transportation policies for single and multi-objective transportation problem using fuzzy logic.Math. Comput. Model.2011539-101637164610.1016/j.mcm.2010.12.029
    [Google Scholar]
  7. GuptaA. KumarA. A new method for solving linear multi-objective transportation problems with fuzzy parameters.Appl. Math. Model.20123641421143010.1016/j.apm.2011.08.044
    [Google Scholar]
  8. MohanaselviS. GanesanK. Fuzzy optimal solution to fuzzy transportation problem: A new approach.Int. J. Comput. Sci. Eng.201243367
    [Google Scholar]
  9. KhalafW.S. Solving fuzzy transportation problems using a new algorithm.J. Appl. Sci. (Faisalabad)201414325225810.3923/jas.2014.252.258
    [Google Scholar]
  10. EbrahimnejadA. A simplified new approach for solving fuzzy transportation problems with generalized trapezoidal fuzzy numbers.Appl. Soft Comput.20141917117610.1016/j.asoc.2014.01.041
    [Google Scholar]
  11. AhmedN.U. KhanA.R. UddinM.S. Solution of mixed type transportation problem: A fuzzy approach.Buletinul Institutului Politehnic Din Iaşi20156121931
    [Google Scholar]
  12. HusseinI.H. DheyabA.H. A new algorithm using ranking function to find solution for fuzzy transportation problem.IJMSS2015332126
    [Google Scholar]
  13. EbrahimnejadA. New method for solving fuzzy transportation problems with LR flat fuzzy numbers.Inf. Sci.201635710812410.1016/j.ins.2016.04.008
    [Google Scholar]
  14. DhanasekarS. HariharanS. SekarP. Fuzzy hungarian MODI algorithm to solve fully fuzzy transportation problems.Int. J. Fuzzy Syst.20171951479149110.1007/s40815‑016‑0251‑4
    [Google Scholar]
  15. PantM. RayK. SharmaT.K. RawatS. BandyopadhyayA. soft computing: theories and applications.Proc SocTa.vol. Vol. 2. Springer, 2016.
    [Google Scholar]
  16. HunwisaiD. KumamP. A method for solving a fuzzy transportation problem via robust ranking technique and ATM.Cogent Math. Stat.201741
    [Google Scholar]
  17. BaykasoğluA. SubulanK. Constrained fuzzy arithmetic approach to fuzzy transportation problems with fuzzy decision variables.Expert Syst. Appl.20178119322210.1016/j.eswa.2017.03.040
    [Google Scholar]
  18. SteinO. Sudermann-MerxN. The noncooperative transportation problem and linear generalized nash games.Eur. J. Oper. Res.2018266254355310.1016/j.ejor.2017.10.001
    [Google Scholar]
  19. MaheswariP.U. GanesanK. Solving fully fuzzy transportation problem using pentagonal fuzzy numbers.J. Phys. Conf. Ser.201810001121410.1088/1742‑6596/1000/1/012014
    [Google Scholar]
  20. MathurN. SrivastavaP.K. PaulA. Algorithms for solving fuzzy transportation problem.Int. J. Math. Oper.201812219021910.1504/IJMOR.2018.089677
    [Google Scholar]
  21. Sam’anM. SurarsoB. IrwantoB. Solving of fuzzy transportation problem using fuzzy analytical hierarchy process (AHP)Proc. - 2016 2nd Int. Conf. Sci. Technol.-Comput. ICST 2016 (ISSTEC 2019)202010-15
    [Google Scholar]
  22. SrinivasanR. KarthikeyanN. RenganathanK. VijayanD.V. Method for solving fully fuzzy transportation problem to transform the materialsMater. Scivol. Vol. 2214, 2020.
    [Google Scholar]
  23. SivriM. KockenH.G. AlbayrakI. AkinS. Generating a set of compromise solutions of a multi objective linear programming problem through game theoryOperations research and decisions, vol. Vol. 29, 2019.
    [Google Scholar]
  24. YagerR. A procedure for ordering fuzzy subsets of the unit interval.Inf. Sci.198124214316110.1016/0020‑0255(81)90017‑7
    [Google Scholar]
  25. OkadaS. SoperT. A shortest path problem on a network with fuzzy arc lengths.Fuzzy Sets Syst.2020109112914010.1016/S0165‑0114(98)00054‑2
    [Google Scholar]
  26. ChangY.L. DesaiK. A fuzzy linear programing approach in production planning.John Wiley & Sons, Inc.2003
    [Google Scholar]
  27. Senthil KumarP. A simple method for solving type-2 and type-4 fuzzy transportation problems.Int. J. Fuzzy Log. Intell.201616422523710.5391/IJFIS.2016.16.4.225
    [Google Scholar]
  28. ChanasS. KuchtaD. A concept of the optimal solution of the transportation problem with fuzzy cost coefficients.Fuzzy Sets Syst.199682329930510.1016/0165‑0114(95)00278‑2
    [Google Scholar]
/content/journals/flme/10.2174/2666294901666220324121629
Loading
/content/journals/flme/10.2174/2666294901666220324121629
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test