Skip to main content
SpringerLink
Log in

Third-Party Cold Chain Medicine Logistics Provider Selection by a Rough Set-Based Gained and Lost Dominance Score Method

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Choosing third-party cold chain logistics suppliers can be regarded as a multiple criteria group decision-making problem since multiple aspects of suppliers are required to be evaluated by multiple experts. This paper aims to address the problem of selecting the optimal third-party cold chain medicine logistics provider considering the uncertainties caused by the qualitative criteria that are difficult to accurately evaluate and the limitation of experts’ knowledge and cognition. We propose a rough set-based gained and lost dominance score method in which experts are supposed to use linguistic terms to express their information. First, we combine rough numbers with linguistic scale functions to depict the semantics of linguistic terms and convert the formal expression information of linguistic terms into numerical information, which has an advantage of expressing imprecision and subjective judgments of experts. In addition, given that different experts often have different emphases on different attributes, we investigate a rough set-based gained and lost dominance score method in which experts have different weights for different criteria. Finally, an illustrative example of selecting the optimal third-party cold chain medicine logistics is given with comparative analysis, showing the efficiency of the proposed method. This method provides a new way to solve the selection problem of third-party logistics suppliers.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Aguezzoul, A.: Third-party logistics selection problem: a literature review on criteria and methods. Omega 149, 69–78 (2014)

    Article  Google Scholar 

  2. Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning—Part I. Inf. Sci. 8, 199–249 (1975)

    Article  Google Scholar 

  3. Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000)

    Article  Google Scholar 

  4. Rodriguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–119 (2012)

    Article  Google Scholar 

  5. Pang, Q., Wang, H., Xu, Z.S.: Probabilistic linguistic term sets in multi-attribute group decision making. Inf. Sci. 369, 128–143 (2016)

    Article  Google Scholar 

  6. Liao, H.C., Wu, X.L., Liang, X.D., Yang, J.B., Xu, D.L., Herrera, F.: A continuous interval-valued linguistic ORESTE method for multi-criteria group decision making. Knowl.-Based Syst. 153, 65–77 (2018)

    Article  Google Scholar 

  7. Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)

    Article  Google Scholar 

  8. Kacprzyk, J.: On some fuzzy cores and “soft” consensus measures in group decision making. Anal. Fuzzy Inf. 2, 119–130 (1987)

    MathSciNet  MATH  Google Scholar 

  9. Liao, H.C., Xu, Z.S., Herrera-Viedma, E., Herrera, F.: Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey. Int. J. Fuzzy Syst. 20, 2084–2110 (2018)

    Article  MathSciNet  Google Scholar 

  10. Alinezad, A., Seif, A., Esfandiari, N.: Supplier evaluation and selection with QFD and FAHP in a pharmaceutical company. Int. J. Adv. Manuf. Technol. 68, 1–4 (2013)

    Article  Google Scholar 

  11. Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E.K., Antucheviciene, J.: Assessment of third-party logistics providers using a CRITIC-WASPAS approach with interval type-2 fuzzy sets. Transport 32(1), 66–78 (2017)

    Article  Google Scholar 

  12. Wen, Z., Liao, H.C., Ren, R.X., Bai, C.G., Zavadskas, E.K., Antucheviciene, J., Al-Barakati, A.: Cold chain logistics management of medicine with an integrated multi-criteria decision method. Int. J. Environ. Res. Public Health 16(23), 4843 (2019)

    Article  Google Scholar 

  13. Forghani, A., Sadjadi, S.J., Moghadam, B.F.: A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: a case study. PLOS ONE 13(8), e0201604 (2018)

    Article  Google Scholar 

  14. Wu, X.L., Liao, H.C.: A consensus-based probabilistic linguistic gained and lost dominance score method. Eur. J. Oper. Res. 272, 1017–1027 (2019)

    Article  MathSciNet  Google Scholar 

  15. Liao, H.C., Zhang, H.R., Zhang, C., Wu, X.L., Abbas, M.: A q-rung orthopair fuzzy GLDS method for investment evaluation of BE angel capital in China. Technol. Econ. Dev. Econ. 26(1), 103–134 (2019)

    Article  Google Scholar 

  16. Liao, Z.Q., Liao, H.C., Al-Barakati, A.: A Choquet integral-based GLDS method for green supplier selection with hesitant fuzzy information. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds.) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management, ICMSEM2019, Advances in Intelligent Systems and Computing, Springer, Cham, vol. 1001, pp. 273–282 (2019)

  17. Liang, X.D., Wu, X.L., Liao, H.C.: A gained and lost dominance score II method for modelling group uncertainty: case study of site selection of electric vehicle charging stations. J. Clean. Prod. 262, 121239 (2020). https://doi.org/10.1016/j.jclepro.2020.121239

    Article  Google Scholar 

  18. Fu, Z.G., Wu, X.L., Liao, H.C., Herrera, F.: Underground mining method selection with the hesitant fuzzy linguistic gained and lost dominance score method. IEEE Access 6, 66442–66458 (2018)

    Article  Google Scholar 

  19. Liao, H.C., Yu, J.Y., Wu, X.L., Al-Barakati, A., Altalhi, A., Herrera, F.: Life satisfaction evaluation in earthquake-hit area by the probabilistic linguistic GLDS method integrated with the logarithm-multiplicative analytic hierarchy process. Int. J. Disaster Risk Reduct. 38, 101190 (2019)

    Article  Google Scholar 

  20. Xu, Z.S.: Deviation measures of linguistic preference relations in group decision making. Omega 33, 249–254 (2005)

    Article  Google Scholar 

  21. Liao, H.C., Qin, R., Gao, C.Y., Wu, X.L., Hafezalkotob, A., Herrera, F.: Score-HeDLiSF: a score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: an application to unbalanced hesitant fuzzy linguistic MULTIMOORA. Inf. Fusion 48, 39–54 (2019)

    Article  Google Scholar 

  22. Zhai, L.Y., Khoo, L.P., Zhong, Z.W.: A rough set enhanced fuzzy approach to quality function deployment. Int. J. Adv. Manuf. Technol. 37, 613–624 (2008)

    Article  Google Scholar 

  23. Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation of a preference relation by dominance relations. Eur. J. Oper. Res. 117, 63–83 (1999)

    Article  Google Scholar 

  24. Choy, K.L., Chow, H.K., Tan, K.H., Chan, C.K., Mok, E.C., Wang, Q.: Leveraging the supply chain flexibility of third party logistics—hybrid knowledge-based system approach. Expert Syst. Appl. 35, 1998–2016 (2008)

    Article  Google Scholar 

  25. Singh, R.K., Gunasekaran, A., Kumar, P.: Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach. Ann. Oper. Res. 267, 531–553 (2018)

    Article  MathSciNet  Google Scholar 

  26. Jharkharia, S., Shankar, R.: Selection of logistics service provider: an analytic network process (ANP) approach. Omega 35, 274–289 (2007)

    Article  Google Scholar 

  27. Ho, W., He, T., Lee, C.K.M., Emrouznejada, A.: Strategic logistics outsourcing: an integrated QFD and fuzzy AHP approach. Expert Syst. Appl. 39, 10841–10850 (2012)

    Article  Google Scholar 

  28. Min, H., Joo, S.J.: Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis. Supply Chain Manage Int J 11, 259–265 (2006)

    Article  Google Scholar 

  29. Thakkar, J., Deshnukh, S.G., Gupta, A.D., Shankar, R.: Selection of third-party logistics (3PL): a hybrid approach using interpretive structural modeling (ISM) and analytic network process (ANP). In: Supply Chain Forum: An International Journal, Vol. 6, No. 1, Taylor, Francis (2005)

  30. Liu, H.T., Wang, W.K.: An integrated fuzzy approach for provider evaluation and selection in third-party logistics. Expert Syst. Appl. 36, 4387–4398 (2009)

    Article  Google Scholar 

  31. Pamučar, D., Mihajlović, M., Obradović, R., Atanasković, P.: Novel approach to group multi-criteria decision making based on interval rough numbers: hybrid DEMATEL-ANP-MAIRCA model. Expert Syst. Appl. 88, 58–80 (2017)

    Article  Google Scholar 

  32. Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., Matić, B.: Evaluation of a third-party logistics (3PL) provider using a rough SWARA–WASPAS model based on a new rough Dombi aggregator. Symmetry 10, 305 (2018)

    Article  Google Scholar 

  33. Mcginnis, M.A., Kochunny, C.M., Ackerman, K.B.: Third party logistics choice. Int. J. Logist. Manage. 6, 93–102 (1995)

    Article  Google Scholar 

  34. Moberg, C.R., Speh, T.W.: Third-party warehousing selection: a comparison of national and regional firms. Am. J. Bus. 19, 71–76 (2004)

    Article  Google Scholar 

  35. Halldórsson, Á., Kovács, G., Wolf, C., Seuring, S.: Environmental impacts as buying criteria for third party logistical services. Int. J. Phys. Distrib. Logist. Manage. 40, 84–102 (2010)

    Article  Google Scholar 

  36. Montanari, R.: Cold chain tracking: a managerial perspective. Trends Food Sci. Technol. 19, 425–431 (2008)

    Article  Google Scholar 

  37. Eklund, P., Rusinowska, A., Swart, H.D.: Consensus reaching in committees. Eur. J. Oper. Res. 178, 185–193 (2007)

    Article  Google Scholar 

  38. del Moral, M.J., Chiclana, F., Tapia, J.M., Herrera-Viedma, E.: A comparative study on consensus measures in group decision making. Int. J. Intell. Syst. 33, 1624–1638 (2018)

    Article  Google Scholar 

  39. Jaberidoost, M., Nikfar, S., Abdollahiasl, A., Dinarvand, R.: Pharmaceutical supply chain risks: a systematic review. Daru-J. Pharm. Sci. 21, 69 (2013)

    Article  Google Scholar 

  40. Zhu, G.N.: An integrated AHP and VIKOR for design concept evaluation based on rough number. Adv. Eng. Inform. 29, 408–418 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation of China (Nos. 71771156, 71971145).

Author information

Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610064, China

    Huchang Liao, Jiaying Chang & Zhiying Zhang

  2. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Huchang Liao & Abdullah Al-Barakati

  3. School of Finance, Anhui University of Finance and Economics, Bengbu, 233030, China

    Xiang Zhou

Authors
  1. Huchang Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiaying Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdullah Al-Barakati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Huchang Liao or Xiang Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, H., Chang, J., Zhang, Z. et al. Third-Party Cold Chain Medicine Logistics Provider Selection by a Rough Set-Based Gained and Lost Dominance Score Method. Int. J. Fuzzy Syst. 22, 2055–2069 (2020). https://doi.org/10.1007/s40815-020-00867-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-020-00867-0

Keywords

Search

Navigation