Skip to main content
SpringerLink
Log in

Interval Type-2 Fuzzy Logic and Its Application to Profit Maximization Solid Transportation Problem in Mustard Oil Industry

  • Conference paper
  • First Online:
Recent Advances in Intelligent Information Systems and Applied Mathematics (ICITAM 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 863))

Abstract

In this paper, a new design of interval type-2 fuzzy logic systems (IT2FLS) for a profit maximization solid transportation problem is presented. It has been proposed that the antecedent membership function parameters of the IT2FLS are produced randomly with the consequent part parameters being determined practically by the mustard oil producing factory in India. The application of mustered oil factory data sets has been used in order to exhibit the effectiveness of the proposed design of IT2FLS.The unit Transportation cost is determined by using IT2FLS, three inputs- supplies, demands and road conditions and one output parameter. A profit solid transportation problem is then formulated with volume and capacity constraints of oil packets and Generalized Reduced Gradient Technique (LINGO-12.0) is used to solve the proposed model. Lastly the optimal results are presented in tabular form and graphically.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ojha, A., Mondal, S.K., Maiti, M.: Transportation policies for single and multi-objective transportation problem using fuzzy logic. Math. Comput. Modell. 53, 1637–1646 (2011)

    Article  Google Scholar 

  2. Mendel, J., John, R., Liu, F.: Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Syst. 14, 808–821 (2006)

    Article  Google Scholar 

  3. Wu, D.: On the fundamental differences between interval type-2 and type-1 fuzzy logic controllers. IEEE Trans. Fuzzy Syst. 20(5), 832–848 (2012)

    Article  Google Scholar 

  4. Wang, C.H., Cheng, C.S., Lee, T.T.: Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN). IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 34(3), 1462–77 (2004)

    Article  Google Scholar 

  5. Castillo, O., Angulo, L., Castro, J., Valdez, M.: A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Inf. Sci. 354, 257–274 (2016)

    Article  Google Scholar 

  6. Sabahi, K., Ghaemi, S., Badamchizadeh, M.: Designing an adaptive type-2 fuzzy logic system load frequency control for a nonlinear time-delay power system. Appl. Soft Comput. 43, 97–106 (2016)

    Article  Google Scholar 

  7. Salles, K., Neto, A., Marujo, L.: Using fuzzy logic to implement decision policies in system dynamics models. Expert Syst. Appl. 55, 172–183 (2016)

    Article  Google Scholar 

  8. Jana, D.K., Castillo, O., Pramanik, S., Maiti, M.: Application of interval type-2 fuzzy logic to polypropylene business policy in a petrochemical plant in India. J. Saudi Soc. Agric. Sci. (2016)

    Google Scholar 

  9. Hong, Y., Pasmana, H.J., Sachdevaa, S., Markowskib, A.S., Mannan, M.S.: A fuzzy logic and probabilistic hybrid approach to quantify the uncertainty in layer of protection analysis. J. Loss Prev. Process Ind. 43, 10–17 (2016)

    Article  Google Scholar 

  10. Kaur, A., kumar, A.: A new approach for solving fuzzy transportation problems using generalized trapezoidal fuzzy numbers. Appl. Soft Comput. 12, 1201–1213 (2012)

    Article  Google Scholar 

  11. Hirsch, W.M., Dantzig, G.B.: The fixed charge problem. Nav. Res. Logist. Quart. 15, 413–424 (1968)

    Article  MathSciNet  Google Scholar 

  12. Giri, P.K., Maiti, M.K., Maiti, M.: Fuzzy stochastic solid transportation problem using fuzzy goal programming approach. Comput. Ind. Eng. 72, 160–168 (2014)

    Article  Google Scholar 

  13. Hitchcock, F.L.: The distribution of a product from several sources to numerous localities. J. Math. Phys. 20, 224–230 (1941)

    Article  MathSciNet  Google Scholar 

  14. Fegad, M.R., Jadhav, A.V., Minley, A.R.: Finding an optimal solution of transportation problem using interval and triangular membership functions. Eur. J. Oper. Res. 60, 415–421 (2011)

    Google Scholar 

  15. Chanas, S., Kolosziejczyj, W., Machaj, A.: A fuzzy approach to the transportation problem. Fuzzy Sets Syst. 13, 211–221 (1984)

    Article  MathSciNet  Google Scholar 

  16. Yang, L., Liu, P., Li, S., Gao, Y., Ralescu, D.A.: Reduction methods of type-2 uncertain variables and their applications to solid transportation problem. Inf. Sci. 291, 204–237 (2015)

    Article  MathSciNet  Google Scholar 

  17. Castillo, O., Melin, P.: Type-2 Fuzzy Logic: Theory and Applications. Studies in Fuzziness and Soft Computing, vol. 223. Springer, Berlin (2008)

    Book  Google Scholar 

  18. Chakraborty, D., Jana, D.K., Roy, T.K.: Multi-item integrated supply chain model for deteriorating items with stock dependent demand under fuzzy random and bifuzzy environments. Comput. Ind. Eng. 88, 166–180 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology Durgapur, Durgapur, 713209, West Bengal, India

    Palash Sahoo & Goutam Panigrahi

  2. Department of Engineering Science, Haldia Institute of Technology, Haldia, 721631, West Bengal, India

    Dipak Kumar Jana

Authors
  1. Palash Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dipak Kumar Jana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Goutam Panigrahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Palash Sahoo .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Haldia Institute of Technology, Haldia, West Bengal, India

    Dipak Kumar Jana

  3. Maulana Abul Kalam Azad University of Technology, Haringhata, India

    Debasis Giri

  4. Haldia Institute of Technology, Haldia, India

    Arif Ahmed

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sahoo, P., Jana, D.K., Panigrahi, G. (2020). Interval Type-2 Fuzzy Logic and Its Application to Profit Maximization Solid Transportation Problem in Mustard Oil Industry. In: Castillo, O., Jana, D., Giri, D., Ahmed, A. (eds) Recent Advances in Intelligent Information Systems and Applied Mathematics. ICITAM 2019. Studies in Computational Intelligence, vol 863. Springer, Cham. https://doi.org/10.1007/978-3-030-34152-7_2

Download citation

Publish with us

Policies and ethics

Search

Navigation