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A note on 'a new approach for solving intuitionistic fuzzy transportation problem of type 2'
102 Int. J. Logistics Systems and Management, Vol. 29, No. 1, 2018
Copyright © 2018 Inderscience Enterprises Ltd.
A note on ‘a new approach for solving intuitionistic
fuzzy transportation problem of type-2’
P. Senthil Kumar
PG and Research Department of Mathematics,
Jamal Mohamed College (Autonomous),
Tiruchirappalli, Tamilnadu, India
Email: senthilsoft_5760@yahoo.com
Email: senthilsoft1985@gmail.com
Abstract: In real-life decisions, usually we happen to suffer through different
states of uncertainties. In order to counter these uncertainties, in this paper, the
author formulated a transportation problem in which costs are triangular
intuitionistic fuzzy numbers, supplies and demands are crisp numbers. In this
paper, a simple method for solving type-2 intuitionistic fuzzy transportation
problem (type-2 IFTP) is proposed and optimal solution is obtained without
using intuitionistic fuzzy modified distribution method and intuitionistic fuzzy
zero point method. So, the proposed method gives the optimal solution directly.
The solution procedure is illustrated with the help of three real life numerical
examples. Defect of existing results proposed by Singh and Yadav (2016a) is
discussed. Validity of Pandian’s (2014) method is reviewed. Finally, the
comparative study, results and discussion are given.
Keywords: intuitionistic fuzzy set; type-2 intuitionistic fuzzy transportation
problem; triangular intuitionistic fuzzy number; optimal solution.
Reference to this paper should be made as follows: Kumar, P.S. (2018) ‘A note
on ‘a new approach for solving intuitionistic fuzzy transportation problem
of type-2’’, Int. J. Logistics Systems and Management, Vol. 29, No. 1,
pp.102–129.
Biographical notes: P. Senthil Kumar is an Assistant Professor in PG and
Research Department of Mathematics at Jamal Mohamed College
(Autonomous), Tiruchirappalli, Tamil Nadu, India. He has six years of teaching
experience. He received his BSc, MSc and MPhil from Jamal Mohamed
College, Tiruchirappalli in 2006, 2008, 2010, respectively. He completed his
BEd in Jamal Mohamed College of Teacher Education in 2009. He completed
PGDCA in 2011 in the Bharathidasan University and PGDAOR in 2012 in
Annamalai University, Tamil Nadu, India. He has submitted his PhD thesis in
the area of intuitionistic fuzzy optimisation technique to the Bharathidasan
University in 2015. He has published many research papers in referred national
and international journals like Springer, IGI Global, etc. He also presented his
research in Elsevier Conference Proceedings (ICMS-2014), MMASC-2012,
etc. His areas of interest include operations research, fuzzy optimisation,
intuitionistic fuzzy optimisation, numerical analysis and graph theory, etc.
Reference to this paper should be made as follows:
Kumar, P.S. (2018) ‘A note on ‘a new approach for solving intuitionistic fuzzy
transportation problem of type-2’’, Int. J. Logistics Systems and Management,
Vol. 29, No. 1, pp.102–129. DOI: 10.1504/IJLSM.2018.10009204
MLA
Kumar, P. Senthil. "A note on 'a new approach for solving intuitionistic fuzzy
transportation problem of type-2'." International Journal of Logistics Systems
and Management 29.1 (2018): 102-129. DOI: 10.1504/IJLSM.2018.10009204
APA
Kumar, P. S. (2018). A note on 'a new approach for solving intuitionistic fuzzy
transportation problem of type-2'. International Journal of Logistics Systems
and Management, 29(1), 102-129. DOI: 10.1504/IJLSM.2018.10009204
Chicago
Kumar, P. Senthil. "A note on 'a new approach for solving intuitionistic fuzzy
transportation problem of type-2'." International Journal of Logistics Systems
and Management 29, no. 1 (2018): 102-129.
DOI: 10.1504/IJLSM.2018.10009204
Harvard
Kumar, P.S., 2018. A note on 'a new approach for solving intuitionistic fuzzy
transportation problem of type-2'. International Journal of Logistics Systems
and Management, 29(1), pp.102-129. DOI: 10.1504/IJLSM.2018.10009204
Vancouver
Kumar PS. A note on 'a new approach for solving intuitionistic fuzzy
transportation problem of type-2'. International Journal of Logistics Systems
and Management. 2018;29(1):102-29. DOI: 10.1504/IJLSM.2018.10009204
124 P.S. Kumar
0, for 202,
202
, for 202 588,
386
( ) 1, for 588,
1256
, for 588 c 1256,
668
0, for 1256,
IZ
c
c
c
μ c c
c
c
<⎧
⎪ −⎪ ≤ ≤
⎪
⎪
= =⎨
⎪ −
⎪ ≤ ≤
⎪
⎪ >⎩
(3)
and
1, for 74,
588
, for 74 588,
514
( ) 0, for 588,
588
, for 588 1384,
796
1, for 1384.
IZ
c
c
c
c c
c
c
c
<⎧
⎪ −⎪ ≤ ≤
⎪
⎪
= =⎨
⎪ −
⎪ ≤ ≤
⎪
⎪ >⎩
ϑ (4)
7 Conclusions
The type-2 IFTPs are solved by the proposed method which differs from the existing
methods namely, intuitionistic fuzzy modified distribution method and intuitionistic
fuzzy zero point method. The main advantage of this method is that the obtained solution
is always optimal. To apply this method, there is no necessity to have (m + n–1) number
of non-negative allotted entries (i.e., basic feasible solution). Also, we need not test the
optimality condition. It is applicable to type-1, type-2, type-3 and type-4 IFTPs. The
proposed method can help decision-makers in the logistics related issues of real-life
problems by aiding them in the decision-making process and providing an optimal
solution in a simple and effective manner. Further, it can be served as an important tool
for a decision-maker when he/she handles various types of logistic problems having
different types of parameters.
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A note on 'a new approach for solving intuitionistic fuzzy transportation problem of type 2'

  • 2. 102 Int. J. Logistics Systems and Management, Vol. 29, No. 1, 2018 Copyright © 2018 Inderscience Enterprises Ltd. A note on ‘a new approach for solving intuitionistic fuzzy transportation problem of type-2’ P. Senthil Kumar PG and Research Department of Mathematics, Jamal Mohamed College (Autonomous), Tiruchirappalli, Tamilnadu, India Email: senthilsoft_5760@yahoo.com Email: senthilsoft1985@gmail.com Abstract: In real-life decisions, usually we happen to suffer through different states of uncertainties. In order to counter these uncertainties, in this paper, the author formulated a transportation problem in which costs are triangular intuitionistic fuzzy numbers, supplies and demands are crisp numbers. In this paper, a simple method for solving type-2 intuitionistic fuzzy transportation problem (type-2 IFTP) is proposed and optimal solution is obtained without using intuitionistic fuzzy modified distribution method and intuitionistic fuzzy zero point method. So, the proposed method gives the optimal solution directly. The solution procedure is illustrated with the help of three real life numerical examples. Defect of existing results proposed by Singh and Yadav (2016a) is discussed. Validity of Pandian’s (2014) method is reviewed. Finally, the comparative study, results and discussion are given. Keywords: intuitionistic fuzzy set; type-2 intuitionistic fuzzy transportation problem; triangular intuitionistic fuzzy number; optimal solution. Reference to this paper should be made as follows: Kumar, P.S. (2018) ‘A note on ‘a new approach for solving intuitionistic fuzzy transportation problem of type-2’’, Int. J. Logistics Systems and Management, Vol. 29, No. 1, pp.102–129. Biographical notes: P. Senthil Kumar is an Assistant Professor in PG and Research Department of Mathematics at Jamal Mohamed College (Autonomous), Tiruchirappalli, Tamil Nadu, India. He has six years of teaching experience. He received his BSc, MSc and MPhil from Jamal Mohamed College, Tiruchirappalli in 2006, 2008, 2010, respectively. He completed his BEd in Jamal Mohamed College of Teacher Education in 2009. He completed PGDCA in 2011 in the Bharathidasan University and PGDAOR in 2012 in Annamalai University, Tamil Nadu, India. He has submitted his PhD thesis in the area of intuitionistic fuzzy optimisation technique to the Bharathidasan University in 2015. He has published many research papers in referred national and international journals like Springer, IGI Global, etc. He also presented his research in Elsevier Conference Proceedings (ICMS-2014), MMASC-2012, etc. His areas of interest include operations research, fuzzy optimisation, intuitionistic fuzzy optimisation, numerical analysis and graph theory, etc.
  • 3. Reference to this paper should be made as follows: Kumar, P.S. (2018) ‘A note on ‘a new approach for solving intuitionistic fuzzy transportation problem of type-2’’, Int. J. Logistics Systems and Management, Vol. 29, No. 1, pp.102–129. DOI: 10.1504/IJLSM.2018.10009204 MLA Kumar, P. Senthil. "A note on 'a new approach for solving intuitionistic fuzzy transportation problem of type-2'." International Journal of Logistics Systems and Management 29.1 (2018): 102-129. DOI: 10.1504/IJLSM.2018.10009204 APA Kumar, P. S. (2018). A note on 'a new approach for solving intuitionistic fuzzy transportation problem of type-2'. International Journal of Logistics Systems and Management, 29(1), 102-129. DOI: 10.1504/IJLSM.2018.10009204 Chicago Kumar, P. Senthil. "A note on 'a new approach for solving intuitionistic fuzzy transportation problem of type-2'." International Journal of Logistics Systems and Management 29, no. 1 (2018): 102-129. DOI: 10.1504/IJLSM.2018.10009204 Harvard Kumar, P.S., 2018. A note on 'a new approach for solving intuitionistic fuzzy transportation problem of type-2'. International Journal of Logistics Systems and Management, 29(1), pp.102-129. DOI: 10.1504/IJLSM.2018.10009204 Vancouver Kumar PS. A note on 'a new approach for solving intuitionistic fuzzy transportation problem of type-2'. International Journal of Logistics Systems and Management. 2018;29(1):102-29. DOI: 10.1504/IJLSM.2018.10009204
  • 4. 124 P.S. Kumar 0, for 202, 202 , for 202 588, 386 ( ) 1, for 588, 1256 , for 588 c 1256, 668 0, for 1256, IZ c c c μ c c c c <⎧ ⎪ −⎪ ≤ ≤ ⎪ ⎪ = =⎨ ⎪ − ⎪ ≤ ≤ ⎪ ⎪ >⎩ (3) and 1, for 74, 588 , for 74 588, 514 ( ) 0, for 588, 588 , for 588 1384, 796 1, for 1384. IZ c c c c c c c c <⎧ ⎪ −⎪ ≤ ≤ ⎪ ⎪ = =⎨ ⎪ − ⎪ ≤ ≤ ⎪ ⎪ >⎩ ϑ (4) 7 Conclusions The type-2 IFTPs are solved by the proposed method which differs from the existing methods namely, intuitionistic fuzzy modified distribution method and intuitionistic fuzzy zero point method. The main advantage of this method is that the obtained solution is always optimal. To apply this method, there is no necessity to have (m + n–1) number of non-negative allotted entries (i.e., basic feasible solution). Also, we need not test the optimality condition. It is applicable to type-1, type-2, type-3 and type-4 IFTPs. The proposed method can help decision-makers in the logistics related issues of real-life problems by aiding them in the decision-making process and providing an optimal solution in a simple and effective manner. Further, it can be served as an important tool for a decision-maker when he/she handles various types of logistic problems having different types of parameters. References Aggarwal, S. and Gupta, C. (2014) Algorithm for Solving Intuitionistic Fuzzy Transportation Problem with Generalized Trapezoidal Intuitionistic Fuzzy Number via New Ranking Method, Cornell University, Ithaca, USA, arXiv preprint arXiv:1401.3353. Angelov, P.P. (1997) ‘Optimization in an intuitionistic fuzzy environment’, Fuzzy Sets and Systems, Vol. 86, No. 3, pp.299–306. Antony, R.J.P., Savarimuthu, S.J. and Pathinathan, T. (2014) ‘Method for solving the transportation problem using triangular intuitionistic fuzzy number’, International Journal of Computing Algorithm, Integrated Intelligent Research (IIR), Chennai, Tamilnadu, India, Vol. 3, pp.590–605. Atanassov, K.T. (1983) Intuitionistic Fuzzy Sets, VII ITKR’s Session, Sofia, June, Deposed in Central Sci.-Techn. Library of Bulg. Acad. of Sci., 1697/84, in Bulg.
  • 5. A note 125 Atanassov, K.T. (1995) ‘Ideas for intuitionistic fuzzy equations, inequalities and optimization’, Notes on Intuitionistic Fuzzy Sets, Vol. 1, No. 1, pp.17–24. Atanassov, K.T. (1999) Intuitionistic Fuzzy Sets: Theory and Applications, Physica-Verlag, Heidelberg, New York. Ban, A. (2008) ‘Trapezoidal approximation of intuitionistic fuzzy numbers expressed by value, ambiguity, and weighted expected value’, Notes on Intuitionistic Fuzzy Sets, Vol. 14, No. 1, pp.38–47. Basirzadeh, H. (2011) ‘An approach for solving fuzzy transportation problem’, Applied Mathematical Sciences, Vol. 5, No. 32, pp.1549–1566. Bharati, S.K., Nishad, A.K. and Singh, S.R. (2014) ‘Solution of multi-objective linear programming problems in intuitionistic fuzzy environment’, in Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), 28–30 December 2012, Springer, India, January, pp.161–171. Biswas, A. and Modak, N. (2012) ‘Using fuzzy goal programming technique to solve multiobjective chance constrained programming problems in a fuzzy environment’, International Journal of Fuzzy System Applications (IJFSA), Vol. 2, No. 1, pp.71–80, doi: 10.4018/ijfsa.2012010105. Bit, A.K., Biswal, M.P. and Alam, S.S. (1992) ‘Fuzzy programming approach to multicriteria decision making transportation problem’, Fuzzy Sets and Systems, Vol. 50, No. 2, pp.135–141. Burillo, P., Bustince, H. and Mohedano, V. (1994) ‘Some definitions of intuitionistic fuzzy number-first properties’, in Proceedings of the 1st Workshop on Fuzzy Based Expert System, Sofia, Bulgaria, September, pp.53–55. Chakraborty, D., Jana, D.K. and Roy, T.K. (2015) ‘A new approach to solve multi-objective multi- choice multi-item Atanassov’s intuitionistic fuzzy transportation problem using chance operator’, Journal of Intelligent and Fuzzy Systems, Vol. 28, No. 2, pp.843–865. Chanas, S. and Kuchta, D. (1996) ‘A concept of the optimal solution of the transportation problem with fuzzy cost coefficients’, Fuzzy Sets and Systems, Vol. 82, No. 3, pp.299–305. Chanas, S. and Kuchta, D. (1998) ‘Fuzzy integer transportation’, Fuzzy Sets and Systems, Vol. 98, No. 3, pp.291–298. Chanas, S., Delgado, M., Verdegay, J.L. and Vila, M.A. (1993) ‘Interval and fuzzy extension of classical transportation problems’, Transportation Planning and Technology, Vol. 17, No. 2, pp.203–218. Chanas, S., Kolodziejczyk, W. and Machaj, A. (1984) ‘A fuzzy approach to the transportation problem’, Fuzzy Sets and Systems, Vol. 13, No. 3, pp.211–221. Charnes, A. and Cooper, W.W. (1954) ‘The stepping-stone method for explaining linear programming calculation in transportation problem’, Management Science, Vol. 1, No. 1, pp.49–69. Chen, M., Ishii, H. and Wu, C. (2008) ‘Transportation problems on a fuzzy network’, International Journal of Innovative Computing Information and Control, Vol. 4, No. 5, pp.1105–1109. Cherian, L. and Kuriakose, S. (2009) ‘A fuzzy economic production quantity model with capacity constraint: intuitionistic fuzzy optimization for linear programming problems’, The Journal of Fuzzy Mathematics, Vol. 17, No. 1, pp.139–144. Chiang, J. (2005) ‘The optimal solution of the transportation problem with fuzzy demand and fuzzy product’, J. Inf. Sci. Eng, Vol. 21, No. 2, pp.439–451. Dantzig, G.B. (1963) Linear Programming and Extensions, pp.140–144, Princeton Univ. Press, Princeton, New Jersey. Das, M. and Baruah, H.K. (2007) ‘Solution of the transportation problem in fuzzified form’, Journal of Fuzzy Mathematics, Vol. 15, No. 1, pp.79–95. Dubey, D. and Mehra, A. (2011) ‘Linear programming with triangular intuitionistic fuzzy number’, in Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technolog, Vol. 1, No. 1, pp.563–569, Atlantis Press.
  • 6. 126 P.S. Kumar Ebrahimnejad, A. (2012) ‘Cost efficiency measures with trapezoidal fuzzy numbers in data envelopment analysis based on ranking functions: application in insurance organization and hospital’, International Journal of Fuzzy System Applications (IJFSA), Vol. 2, No. 3, pp.51–68, doi: 10.4018/ijfsa.2012070104. Grzegorzewski, P. (2003a) ‘Distance and orderings in a family of intuitionistic fuzzy numbers’, in Proceedings of the 3rd Conference of the European Society for Fuzzy Logic and Technology, Zittau, Germany, September, pp.223–227. Grzegorzewski.P. (2003b) ‘Intuitionistic fuzzy numbers’, Proceedings of the IFSA 2003 World Congress. Guha, D. and Chakraborty, D. (2010) ‘A theoretical development of distance measure for intuitionistic fuzzy numbers’, International Journal of Mathematics and Mathematical Sciences, Hindawi Publishing Corporation, Nasr City, Cairo, Egypt, Vol. 2010, Article ID 949143, 25pp., doi: 10.1155/2010/949143.. Gupta, G., Kumar, A. and Sharma, M.K. (2016) ‘A note on ‘a new method for solving fuzzy linear programming problems based on the fuzzy linear complementary problem (FLCP)’, International Journal of Fuzzy Systems, Vol. 18, No. 2, pp.333–337. Hitchcock, F.L. (1941) ‘The distribution of a product from several sources to numerous localities’, J. Math. Phys, Vol. 20, No. 2, pp.224–230. Hussain, R.J. and Kumar, P.S. (2012a) ‘The transportation problem with the aid of triangular intuitionistic fuzzy numbers’, Proceedings in International Conference on Mathematical Modeling and Applied Soft Computing (MMASC-2012), Coimbatore Institute of Technology Vol. 1, pp.819–825. Hussain, R.J. and Kumar, P.S. (2012b) ‘The transportation problem in an intuitionistic fuzzy environment’, International Journal of Mathematics Research, Vol. 4, No. 4, pp.411–420. Hussain, R.J. and Kumar, P.S. (2012c) ‘Algorithmic approach for solving intuitionistic fuzzy transportation problem’, Applied Mathematical Sciences, Vol. 6, No. 80, pp.3981–3989. Hussain, R.J. and Kumar, P.S. (2013) ‘An optimal more-for-less solution of mixed constraints intuitionistic fuzzy transportation problems’, Int. J. Contemp. Math. Sciences, Vol. 8, No. 12, pp.565–576, http://dx.doi.org/10.12988/ijcms.2013.13056. Jana, B. and Roy, T.K. (2007) ‘Multi-objective intuitionistic fuzzy linear programming and its application in transportation model’, Notes on Intuitionistic Fuzzy Sets, Vol. 13, No. 1, pp.34–51. Klir, G.J. and Yuan.B. (2003) Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, New York. Koopmans T.C. (1949) ‘Optimum utilization of the transportation system’, Econometrica, Supplement: Report of the Washington Meeting, July, The Econometric Society, Vol. 17, pp.136–146, doi: 10.2307/1907301, http://www.jstor.org/stable/1907301. Kumar, A., Kaur, A. and Gupta, A. (2011) ‘Fuzzy linear programming approach for solving fuzzy transportation problems with transshipment’, J. Math. Model. Algor, Vol. 10, No. 2, pp.163–180. Kumar, P.S. (2016) ‘PSK method for solving type-1 and type-3 fuzzy transportation problems’, International Journal of Fuzzy System Applications (IJFSA), Vol. 5, No. 4, Article 6, article in press. Kumar, P.S. and Hussain, R.J. (2014a) ‘A systematic approach for solving mixed intuitionistic fuzzy transportation problems’, International Journal of Pure and Applied Mathematics, Vol. 92, No. 2, pp.181–190. Kumar, P.S. and Hussain, R.J. (2014b) ‘A method for finding an optimal solution of an assignment problem under mixed intuitionistic fuzzy environment’, Proceedings in International Conference on Mathematical Sciences (ICMS-2014), Published by Elsevier, Sathyabama University, ISBN: 978-93-5107-261-4: 417–421.
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