10.30495/fomj.2022.1953705.1066

Coping Uncertainty in the Supplier Selection Problem Using a Scenario- Based Approach and Distance Measure on Type-2 Intuitionistic Fuzzy Sets

  1. Department of Management, Ayandegan Institute of Higher Education, Tonekabon, Iran

Received: 2022-02-26

Revised: 2022-02-16

Accepted: 2022-05-01

Published in Issue 2022-12-15

How to Cite

Sorourkhah, A. (2022). Coping Uncertainty in the Supplier Selection Problem Using a Scenario- Based Approach and Distance Measure on Type-2 Intuitionistic Fuzzy Sets. Fuzzy Optimization and Modeling Journal (FOMJ), 3(4), 1-8. https://doi.org/10.30495/fomj.2022.1953705.1066

PDF views: 40

Abstract

Supplier selection (SS) is a process in which companies identify, evaluate, and select suppliers. The MCDM methods are often used for supplier selection in supply chain management. An unlimited number of MCDM techniques, such as analytic hierarchy process (AHP), analytic network process (ANP), the technique of order preference distance to the ideal solution (TOPSIS), etc., have been deployed to solve the supplier selection problems. Though they can manage problem complexity, MCDM techniques cannot deal with problem uncertainty. Hence, they have been combined with the fuzzy set, intuitionistic fuzzy set, etc., to provide more accurate solutions to supplier selection problems. Nonetheless, the future uncertainty related to the environmental changes is ignored in the SS literature. Therefore, we use future scenarios as criteria to select the best supplier in this study. Moreover, we applied a distance  measure to rank Type-2 Intuitionistic Fuzzy Set, which is a suitable approach to deal with the vagueness of verbal judgments. A numerical example explains the 5-step proposed approach. 

Keywords

  • Uncertainty,
  • Supplier Selection Scenario,
  • Intuitionistic Fuzzy Sets,
  • Distance Measure

References

  1. Alavi, B., Tavana, M., & Mina, H. (2021). A dynamic decision support system for sustainable supplier selection in circular economy. Sustainable Production and Consumption, 27, 905-920.
  2. Alikhani, R., Torabi, S.A., & Altay, N. (2019). Strategic supplier selection under sustainability and risk criteria. International Journal of Production Economics, 208, 69-82.
  3. Alkahtani, M., & Kaid, H. (2018). Supplier selection in supply chain management: a review study. Int. J. Bus. Perform. Supply Chain Model., 10, 107-130
  4. Anusha, V., & Sireesha, V. (2022). A new distance measure to rank type-2 intuitionistic fuzzy sets and its application to multi-criteria group decision making. International Journal of Fuzzy System Applications (IJFSA), 11(1), 1-17.
  5. Aouadni, S., Aouadni, I., & Rebaï, A. (2019). A systematic review on supplier selection and order allocation problems. Journal of Industrial Engineering International, 15(1), 267-289.
  6. Asadabadi, M.R. (2018). The stratified multi-criteria decision-making method. Knowledge-Based Systems, 162, 115-123.
  7. Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
  8. Aydemir, S.B., & Kaya, T. (2021). TOPSIS method for multi-attribute group decision making based on neutrality aggregation operator under single valued neutrosophic environment: A case study of airline companies. In F. Smarandache and M. Abdel-Basset, Neutrosophic Operational Research: Methods and Applications (pp. 471-492). Cham: Springer International Publishing.
  9. Bakeshlou, E.A., Khamseh, A.A., Asl, M.A.G., Sadeghi, J., & Abbaszadeh, M. (2017). Evaluating a green supplier selection problem using a hybrid MODM algorithm. Journal of Intelligent Manufacturing, 28(4), 913-927.
  10. Bhat, S.A., Singh, A., & Qudaimi, A.A. (2021). A new pythagorean fuzzy analytic hierarchy process based on interval-valued pythagorean fuzzy numbers. Fuzzy Optimization and Modeling Journal, 2(4), 38-51.
  11. Chai, J., & Ngai, E.W.T. (2020). Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead. Expert Systems with Applications, 140, 112903.
  12. Chakraborty, S., Chattopadhyay, R., & Chakraborty, S. (2020). An integrated D-MARCOS method for supplier selection in an iron and steel industry. Decision Making: Applications in Management and Engineering, 3(2), 49-69.
  13. Çuvalcıoğlu, G., & Varol, S.U. (2021). Decision making progress for serving restaurants using intuitionistic fuzzy set theory via controlled sets Journal of Universal Mathematics, 4(2), 296-325.
  14. Da Silva, I.A., Bedregal, B., Bedregal, B., & Santiago, R.H.N. (2021). An interval-valued atanassov’s intuitionistic fuzzy multi-attribute group decision making method based on the best representation of the WA and OWA operators. Journal of Fuzzy Extension and Applications, 2(3), 239-261.
  15. Das, A.K., & Granados, C. (2022). IFP-intuitionistic multi fuzzy N-soft set and its induced IFP-hesitant N-soft set in decision-making. Journal of Ambient Intelligence and Humanized Computing.
  16. Das, S.K., & Edalatpanah, S.A. (2020). A new ranking function of triangular neutrosophic number and its application in integer programming. International Journal of Neutrosophic Science, 4(2), 82-92.
  17. De, S.K., Biswas, R., & Roy, A.R. (2000). Some operations on intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114(3), 477-484.
  18. Dutta, P., Jaikumar, B., & Arora, M.S. (2021). Applications of data envelopment analysis in supplier selection between 2000 and 2020: A literature review. Annals of Operations Research.
  19. Ecer, F. (2022). Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: A case study of a home appliance manufacturer. Operational Research, 22(1), 199-233.
  20. Fallahpour, A., Wong, K.Y., Rajoo, S., Fathollahi-Fard, A.M., Antucheviciene, J., & Nayeri, S. (2021). An integrated approach for a sustainable supplier selection based on Industry 4.0 concept. Environmental Science and Pollution Research.
  21. Giannakis, M., Dubey, R., Vlachos, I., & Ju, Y. (2020). Supplier sustainability performance evaluation using the analytic network process. Journal of Cleaner Production, 247, 119439.
  22. Gohain, B., Chutia, R., & Dutta, P. (2022). Distance measure on intuitionistic fuzzy sets and its application in decision-making, pattern recognition, and clustering problems. International Journal of Intelligent Systems, 37(3), 2458-2501.
  23. Gören, H.G. (2018). A decision framework for sustainable supplier selection and order allocation with lost sales. Journal of Cleaner Production, 183, 1156-1169.
  24. Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: A literature review. Journal of Cleaner Production, 98, 66-83.
  25. Gupta, S., Soni, U., & Kumar, G. (2019). Green supplier selection using multi-criterion decision making under fuzzy environment: A case study in automotive industry. Computers & Industrial Engineering, 136, 663-680.
  26. Kannan, D., Jabbour, A.B.L.d.S., & Jabbour, C.J.C. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233(2), 432-447.
  27. Karsak, E.E., & Dursun, M. (2016). Taxonomy and review of non-deterministic analytical methods for supplier selection. International Journal of Computer Integrated Manufacturing, 29(3), 263-286.
  28. Levandowsky, M., & Winter, D. (1971). Distance between sets. Nature, 234(5323), 34-35.
  29. Liu, A., Xiao, Y., Lu, H., Tsai, S.-B., & Song, W. (2019). A fuzzy three-stage multi-attribute decision-making approach based on customer needs for sustainable supplier selection. Journal of Cleaner Production, 239, 118043.
  30. Mendel, J.M. (2001). Uncertain rule-based fuzzy logic system: Introduction and new directions. (2). Springer, Cham.
  31. Mohan, S., Kannusamy, A.P., & Samiappan, V. (2020). A new approach for ranking of intuitionistic fuzzy numbers. Journal of Fuzzy Extension and Applications, 1(1), 15-26.
  32. Naqvi, M.A., & Amin, S.H. (2021). Supplier selection and order allocation: A literature review. Journal of Data, Information and Management, 3(2), 125-139.
  33. Nezhadroshan, A.M., Fathollahi-Fard, A.M., & Hajiaghaei-Keshteli, M. (2021). A scenario-based possibilistic-stochastic programming approach to address resilient humanitarian logistics considering travel time and resilience levels of facilities. International Journal of Systems Science: Operations & Logistics, 8(4), 321-347.
  34. Rezaei, A., Rahiminezhad Galankashi, M., Mansoorzadeh, S., & Mokhatab Rafiei, F. (2020). Supplier selection and order allocation with lean manufacturing criteria: An integrated MCDM and bi-objective modelling approach. Engineering Management Journal, 32(4), 253-271.
  35. Santos Arteaga, F.J., Ebrahimnejad, A., & Zabihi, A. (2021). A new approach for solving intuitionistic fuzzy data envelopment analysis problems. Fuzzy Optimization and Modeling Journal, 2(2), 46-57.
  36. Singh, S., & Garg, H. (2017). Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process. Applied Intelligence, 46(4), 788-799.
  37. Sorourkhah, A., Azar, A., Babaie-Kafaki, S., & Shafiei Nik Abadi, M. (2017). Using Weighted-robustness analysis in strategy selection (Case study: Saipa Automotive Research and Innovation Center). Industrial Management Journal, 9(4), 665-690. (In Persian).
  38. Sorourkhah, A., Babaie-Kafaki, S., Azar, A., & Shafiei-Nikabadi, M. (2018). Matrix ‎a‎pproach to ‎robustness ‎analysis for ‎strategy ‎selection. International Journal of Industrial Mathematics, 10(3), 261-269.
  39. Sorourkhah, A., Babaie-Kafaki, S., Azar, A., & Shafiei-Nikabadi, M. (2019). A fuzzy-weighted approach to the problem of selecting the right strategy using the robustness analysis (Case study: Iran Automotive Industry). Fuzzy Information and Engineering, 11(1), 39-53.
  40. Sorourkhah, A., & Edalatpanah, S.A. (2021). Considering the criteria interdependency in the matrix approach to robustness analysis with applying fuzzy ANP. Fuzzy Optimization and Modeling Journal, 3(2), 22-33.
  41. Sorourkhah, A., & Edalatpanah, S.A. (2022). Using a combination of matrix approach to robustness analysis (MARA) and fuzzy DEMATEL-based ANP (FDANP) to choose the best decision. International Journal of Mathematical, Engineering and Management Sciences, 7(1), 68-80.
  42. Tuncalı Yaman, T., & Akkartal, G.R. (2022). How warehouse location decisions changed in medical sector after pandemic? A fuzzy comparative study. Journal of Fuzzy Extension and Applications, 3(1), 81-95.
  43. You, X.-Y., You, J.-X., Liu, H.-C., & Zhen, L. (2015). Group multi-criteria supplier selection using an extended VIKOR method with interval 2-tuple linguistic information. Expert Systems with Applications, 42(4), 1906-1916.
  44. Zhang, C. (2018). Research of the selection of green material suppliers based on entropy- TOPSIS model. IOP Conference Series: Materials Science and Engineering, 394, 052063.
  45. Zhang, J., Yang, D., Li, Q., Lev, B., & Ma, Y. (2020). Research on sustainable supplier selection based on the rough DEMATEL and FVIKOR methods. Sustainability, 13, 88.