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Solving a Fully Intuitionistic Fuzzy Transportation Problem Using a Hybrid Multi-Objective Optimization Approach

Author

Listed:
  • Sadegh Niroomand

    (Department of Industrial Engineering, Firouzabad Higher Education Center, Shiraz University of Technology, Shiraz 7155713876, Iran)

  • Tofigh Allahviranloo

    (Research Center of Performance and Productivity Analysis, Istinye University, Istanbul 34010, Turkey
    Quantum Technologies Research Center (QTRC), Science and Research Branch, Islamic Azad University, Tehran 1477893780, Iran)

  • Ali Mahmoodirad

    (Department of Mathematics, Babol Branch, Islamic Azad University, Babol 3738147471, Iran)

  • Alireza Amirteimoori

    (Research Center of Performance and Productivity Analysis, Istinye University, Istanbul 34010, Turkey)

  • Leo Mršić

    (Department of Technical Sciences, Algebra University, Gradiscanska 24, 10000 Zagreb, Croatia
    Rudolfovo Science and Technology Centre, Podbreznik 15, 8000 Novo Mesto, Slovenia)

  • Sovan Samanta

    (Research Center of Performance and Productivity Analysis, Istinye University, Istanbul 34010, Turkey
    Department of Technical Sciences, Algebra University, Gradiscanska 24, 10000 Zagreb, Croatia
    Department of Mathematics, Tamralipta Mahavidyalaya, Tamluk 721636, India)

Abstract

In this study, a typical transportation problem involving intuitionistic fuzzy-type variables and parameters is focused on. The approaches proposed in the literature for such transportation problems have many shortcomings, such as the use of ranking functions and obtaining an infeasible solution with negative values for variables and objective functions in the presence of non-negative unit transportation charges. To overcome such weaknesses, a new approach without a ranking function is introduced in this paper. The proposed approach first constructs an equivalent crisp multi-objective form of the intuitionistic fuzzy transportation problem and then proposes a new hybrid multi-objective solution procedure to tackle the obtained crisp multi-objective problem. The conducted computer experiments with benchmark problems from the existing studies of the literature reflect the effectiveness of the proposed solution approach of this study in terms of the quality of the results when compared to the available approaches of the literature.

Suggested Citation

  • Sadegh Niroomand & Tofigh Allahviranloo & Ali Mahmoodirad & Alireza Amirteimoori & Leo Mršić & Sovan Samanta, 2024. "Solving a Fully Intuitionistic Fuzzy Transportation Problem Using a Hybrid Multi-Objective Optimization Approach," Mathematics, MDPI, vol. 12(24), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:3898-:d:1541055
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    References listed on IDEAS

    as
    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. P. Senthil Kumar & R. Jahir Hussain, 2016. "Computationally simple approach for solving fully intuitionistic fuzzy real life transportation problems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 90-101, December.
    3. K. Ganesan & P. Veeramani, 2006. "Fuzzy linear programs with trapezoidal fuzzy numbers," Annals of Operations Research, Springer, vol. 143(1), pages 305-315, March.
    4. Marc Asunción & Luis Castillo & Juan Fernández-Olivares & Oscar García-Pérez & Antonio González & Francisco Palao, 2007. "Handling fuzzy temporal constraints in a planning environment," Annals of Operations Research, Springer, vol. 155(1), pages 391-415, November.
    5. Sujeet Kumar Singh & Shiv Prasad Yadav, 2016. "A new approach for solving intuitionistic fuzzy transportation problem of type-2," Annals of Operations Research, Springer, vol. 243(1), pages 349-363, August.
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