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Supplier selection using a flexible interval-valued fuzzy VIKOR

  • Iman Mohamad SharafEmail author
Original Paper
  • 49 Downloads

Abstract

One of the major issues in a supply chain (SC) is the selection of the appropriate supplier. Supplier selection (SS) plays a vital role in achieving an effective and successful SC, hence gaining a competitive advantage. This article proposes a novel flexible multi-attribute group decision-making method for SS based on VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) using interval-valued fuzzy sets (IVFSs). The method avoids defuzzification, minimizes the computations, and considers the decision-makers’ optimism level. Avoiding defuzzification keeps the characteristics of (IVFSs) and prevents the loss of information. To minimize the computations, two main modifications are done. While all VIKOR-based techniques use both the best and the worst solutions; the proposed VIKOR uses the best solution only, and the division operations by the difference between two fuzzy sets to compute the separation measures and the index Q are eliminated. Ranking plays a crucial role in VIKOR, since three ranking lists are required. The signed distance is modified and used for ranking due to its simple, few computations. None of the previously VIKOR-based techniques accounted for the decision-makers’ optimism level. Therefore, the signed distance is used in its basic form to keep the α-level explicitly in the ranking formula. Thus, the proposed technique preserves fuzziness, reduces the computations substantially, and allows the participation of the decision-makers through the optimism level. Two examples are solved: one for illustration and the other to compare the results with previously used methods.

Keywords

Interval-valued fuzzy sets VIKOR method Supplier selection 

Notes

Acknowledgements

The author would like to thank the reviewers for the constructive comments and suggestions that immensely improved the presentation of the manuscript.

References

  1. Adeinat H, Ventura JA (2018) Integrated pricing and supplier selection in a two-stage supply chain. Int J Prod Econ 201:193–202CrossRefGoogle Scholar
  2. Chan FTS, Kumar N (2007) Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega 35:417–431CrossRefGoogle Scholar
  3. Chatterjee K, Kar S (2017) Unified granular-number-based AHP-VIKOR multi-criteria decision framework. Granul Comput 2:199–221CrossRefGoogle Scholar
  4. Chen C-T (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114:1–9zbMATHCrossRefGoogle Scholar
  5. Chen T-Y (2012) Multiple criteria group decision-making with generalized interval-valued fuzzy numbers based on signed distances and incomplete weights. Appl Math Model 36:3029–3052MathSciNetzbMATHCrossRefGoogle Scholar
  6. Chen S-M, Chang Y-C (2011) Weighted fuzzy rule interpolation based on GA-based weight-learning techniques. IEEE Trans Fuzzy Syst 19(4):729–744MathSciNetCrossRefGoogle Scholar
  7. Chen S-M, Hsiao W-H (2000) Bidirectional approximate reasoning for rule-based systems using interval-valued fuzzy sets. Fuzzy Sets Syst 113:185–203MathSciNetzbMATHCrossRefGoogle Scholar
  8. Chen S-M, Huang C-M (2003) Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms. IEEE Trans Fuzzy Syst 11(4):495–506CrossRefGoogle Scholar
  9. Chen S-M, Tanuwijaya K (2011) Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Syst Appl 38(2011):15425–15437CrossRefGoogle Scholar
  10. Chen S-M, Hsiao W-H, Jong W-T (1997) Bidirectional approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets Syst 91(3):339–353MathSciNetzbMATHCrossRefGoogle Scholar
  11. Chen S-M, Chang Y-C, Pan J-S (2012a) Fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. IEEE Trans Fuzzy Syst 21(3):412–425CrossRefGoogle Scholar
  12. Chen S-M, Chu H-P, Sheu T-W (2012b) TAIEX forecasting using fuzzy time series and automatically generated weights of multiple factors. IEEE Trans Syst Man Cybern Part A Syst Hum 42(6):1485–1495CrossRefGoogle Scholar
  13. Chen S-M, Manalu GMT, Pan J-S, Liu H-C (2013) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Trans Cybern 43(3):1102–1117CrossRefGoogle Scholar
  14. Cheng S-H, Chen S-M, Jian W-S (2016) Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf Sci 327:272–287MathSciNetzbMATHCrossRefGoogle Scholar
  15. Chiang J (2001) Fuzzy linear programming based on statistical confidence interval and interval-valued fuzzy set. Eur J Oper Res 129(1):65–86MathSciNetzbMATHCrossRefGoogle Scholar
  16. Dickson GW (1996) An analysis of vendor selection systems and decisions. J Purch 2(1):5–17CrossRefGoogle Scholar
  17. Dymova L, Sevastjanov P, Tikhonenko A (2015) An interval type-2 fuzzy extension of the TOPSIS methods using alpha cuts. Knowl Based Syst 83:116–127CrossRefGoogle Scholar
  18. Figueroa-Garcia JC, Chalco-Cano YC, Roman-Florez H (2015) Distance measures for interval-type-2 fuzzy numbers. Discrete Appl Math 197:93–102MathSciNetzbMATHCrossRefGoogle Scholar
  19. Ghorabaee MK (2016) Developing an MCDM method for robot selection with interval type-2 fuzzy sets. Robot Comput Integr Manuf 37:221–232CrossRefGoogle Scholar
  20. Gorzalczany MB (1987) A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets Syst 21:1–17MathSciNetzbMATHCrossRefGoogle Scholar
  21. Gul M, Celik E, Aydin N, Gumus AT, Guneri AF (2016) A state of the art literature review of VIKOR and its fuzzy extensions on applications. Appl Soft Comput 46:60–89CrossRefGoogle Scholar
  22. Heiderzade A, Mahdavi I, Mahdavi-Amiri N (2016) Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: a case study. Appl Soft Comput 38:213–231CrossRefGoogle Scholar
  23. Hsu HM, Chen CT (1997) Fuzzy credibility relation method for multiple criteria decision-making problems. Inf Sci 96:79–91zbMATHCrossRefGoogle Scholar
  24. Ju Y, Wang A (2013) Extension of VIKOR method for multi-criteria group decision-making problem with linguistic information. Appl Math Model 37:3112–3125MathSciNetzbMATHCrossRefGoogle Scholar
  25. Kohout LJ, Bandler W (1996) Fuzzy interval inference utilizing the checklist paradigm and BK-relational products. In: Kearfort RB et al (eds) Applications of interval computations. Kluwer, Dordrecht, pp 291–335zbMATHCrossRefGoogle Scholar
  26. Kumar GK, Rao MS, Kesava Rao VVS (2018) Supplier selection and order allocation in supply chain. Mater Today Proc 5(5):12161–12173CrossRefGoogle Scholar
  27. Lee L-W, Chen S-M (2008) Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations. Expert Syst Appl 34:2763–2771CrossRefGoogle Scholar
  28. Liang Q, Mendel J (2000) Interval-type 2 fuzzy logic systems: theory and design. IEEE Trans Fuzzy Syst 8(5):535–550CrossRefGoogle Scholar
  29. Lima Junior FR, Osiro L, Carpinetti LCR (2014) A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Appl Soft Comput 21:194–209CrossRefGoogle Scholar
  30. Liu J, Liang Y (2017) Multi-granularity unbalanced linguistic group decision-making with incomplete weight information based on VIKOR method. Granul Comput 3(3):219–228CrossRefGoogle Scholar
  31. Liu K, Liu Y, Qin J (2018a) An integrated ANP-VIKOR methodology for supplier selection with interval type-2 fuzzy sets. Granul Comput 3(3):193–208CrossRefGoogle Scholar
  32. Liu S, Xu Z, Gao J (2018b) A fuzzy compromise programming model based on the modified S-curve membership functions for supplier selection. Granul Comput 3(4):275–283CrossRefGoogle Scholar
  33. Mavi RK, Goh M, Mavi NK (2016) Supplier selection with Shannon entropy and fuzzy TOPSIS context of supply chain risk management. In: 12th International strategic conference, ISMC 2016, October 2016, Antalya, TurkeyGoogle Scholar
  34. Mehbodniya A, Kaleem F, Yen KK, Adachi F (2013) A fuzzy extension of VIKOR for target network selection in heterogeneous wireless environments. Phys Commun 7:145–155CrossRefGoogle Scholar
  35. Mendel JM (2016) A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of linguistic term for computing with words. Granul Comput 1:59–69CrossRefGoogle Scholar
  36. Niewiadomski A (2007) Interval-Valued and Interval Type-2 Fuzzy Sets: A Subjective Comparison. IEEE International Fuzzy Systems Conference. IEEE, London, UKGoogle Scholar
  37. Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L (2017) A knowledge-based system for breast cancer classification using fuzzy logic method. Telemat Inform 34(4):133–144CrossRefGoogle Scholar
  38. Opricovic S (1998) Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, BelgradeGoogle Scholar
  39. Opricovic S (2011) Fuzzy VIKOR with an application to water resources planning. Expert Syst Appl 38:12983–12990CrossRefGoogle Scholar
  40. Opricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156:445–455zbMATHCrossRefGoogle Scholar
  41. Ordoobadi SM (2009) Development of a supplier selection model using fuzzy logic. Supply Chain Manag Int J 14(4):314–327CrossRefGoogle Scholar
  42. Pedrycz W (1991) Fuzzy logic in development of fundamentals of pattern recognition. Int J Approx Reason 5(3):251–264MathSciNetzbMATHCrossRefGoogle Scholar
  43. Phochanikorn P, Tan C, Chen W (2019) Barriers analysis for reverse logistics in Thailand’s palm oil industry using fuzzy multi-criteria decision-making method for prioritizing the solutions. Granul Comput.  https://doi.org/10.1007/s41066-019-00155-9 CrossRefGoogle Scholar
  44. Ploskas N, Papathanasiou J, Tsaples G (2017) Implementation of an extended fuzzy VIKOR method based on triangular and trapezoidal fuzzy linguistic variables and alternative defuzzification techniques. In: Linden I, Liu S, Colot C (eds) Decision support systems VII. Data, Information and Knowledge Visualization in Decision Support Systems. ICDSST 2017. Lecture Notes in Business Information Processing. Springer, Cham, vol 282, pp 165–178Google Scholar
  45. Rashid T, Beg I, Husnine SM (2014) Robot selection by using generalized interval-valued fuzzy numbers with TOPSIS. Appl Soft Comput 21:462–468CrossRefGoogle Scholar
  46. Sanayei A, Farid SM, Yazdankhah A (2010) Group decision-making process for supplier selection with VIKOR under fuzzy environment. Expert Syst Appl 37(1):24–30CrossRefGoogle Scholar
  47. Sari K (2017) A novel multi-criteria decision framework for evaluating green supply chain management practices. Comput Ind Eng 105:338–347CrossRefGoogle Scholar
  48. Sayadi MK, Heydari M, Shahanaghi K (2009) Extension of VIKOR method for decision- making problem with interval numbers. Appl Math Model 33:2257–2262MathSciNetzbMATHCrossRefGoogle Scholar
  49. Shemshadi A, Shirazi H, Toreihi M, Tarokh MJ (2011) A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Syst Appl 38:12160–12167CrossRefGoogle Scholar
  50. Shureshjani RA, Darehmiraki M (2013) A new parametric method for ranking fuzzy numbers. Indag Math 24:518–529MathSciNetzbMATHCrossRefGoogle Scholar
  51. Sola HB, Fernandez J, Hagras H, Herrera F, Pagola M, Barrenechea E (2015) Interval type 2fuzzy sets: toward a wider view on their relationship. IEEE Trans Fuzzy Syst 23(5):1876–1882CrossRefGoogle Scholar
  52. Stevenson WJ (2005) Operations management, 8th edn. McGraw Hill, New YorkGoogle Scholar
  53. Sumbac R (1975) Function Φ-Flous, Application a l’aide au diagnostic en pathologie thyroidienne. Thèse de Doctorate en Medicine, Séction Medecine University of Marseille, Marseille, FranceGoogle Scholar
  54. Türk S, John R, Özcan E (2014) Interval type-2 fuzzy sets in supplier selection. In: 14th UK workshop on computational intelligence (UKCI), Bradford, UK 8–10 Sept. 2014Google Scholar
  55. Türkşen IB (1986) Interval-valued strict sets based on normal forms. Fuzzy Sets Syst 20:183–195CrossRefGoogle Scholar
  56. Türkşen IB (1996) Interval-valued strict preference with Zadeh triples. Fuzzy Sets Syst 20:191–210MathSciNetzbMATHCrossRefGoogle Scholar
  57. Vahdani B, Hadipour H, Sadaghiani JS, Amiri M (2010) Extension of VIKOR method based on interval-valued fuzzy sets. Int Adv Manuf Technol 47:1231–1239CrossRefGoogle Scholar
  58. Van Laarhoven PJM, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11:229–241MathSciNetzbMATHCrossRefGoogle Scholar
  59. Yazdani M, Graeml FR (2014) VIKOR and its applications: a state-of-the-art survey. Int J Strateg Decis Sci 5(2):56–83CrossRefGoogle Scholar
  60. Zadeh LH (1965) Fuzzy sets. Inf Control 8(3):338–353zbMATHCrossRefGoogle Scholar
  61. Zadeh LH (1975) The concept of a linguistic variable and its applications to approximate reasoning. Inf Sci 8:199–249MathSciNetzbMATHCrossRefGoogle Scholar
  62. Zhang H, Zhang W, Mei C (2009) Entropy of interval-valued fuzzy sets based on distance and its relationship with similarity measure. Knowl Based Syst 22:449–454CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Basic SciencesHigher Technological InstituteTenth of Ramadan CityEgypt

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