As of August 2024, International Journal "Notes on Intuitionistic Fuzzy Sets" is being indexed in Scopus.
Please check our Instructions to Authors and send your manuscripts to nifs.journal@gmail.com. Next issue: September/October 2024.
Open Call for Papers: International Workshop on Intuitionistic Fuzzy Sets • 13 December 2024 • Banska Bystrica, Slovakia/ online (hybrid mode).
Deadline for submissions: 16 November 2024.

Issue:Pharmaceutical 3PL supplier selection using interval-valued intuitionistic fuzzy TOPSIS

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to navigation Jump to search
shortcut
http://ifigenia.org/wiki/issue:nifs/28/3/361-374
Title of paper: Pharmaceutical 3PL supplier selection using interval-valued intuitionistic fuzzy TOPSIS
Author(s):
Cengiz Kahraman
Department of Industrial Engineering, Istanbul Technical University, Besiktas, Istanbul, Türkiye
kahramanc@itu.edu.tr
Selçuk Çebi
Department of Industrial Engineering, Istanbul Technical University, Besiktas, Istanbul, Türkiye
scebi@itu.edu.tr
Sezi Çevik Onar
Department of Industrial Engineering, Istanbul Technical University, Besiktas, Istanbul, Türkiye
cevikse@itu.edu.tr
Başar Öztayşi
Department of Industrial Engineering, Istanbul Technical University, Besiktas, Istanbul, Türkiye
oztaysib@yildiz.edu.tr
Presented at: 25th ICIFS, Sofia, 9—10 September 2022
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 28 (2022), Number 3, pages 361–374
DOI: https://doi.org/10.7546/nifs.2022.28.3.361-374
Download:  PDF (943  Kb, File info)
Abstract: Third party logistics (3PL) supplier selection problem is a multi-criteria selection problem that is frequently discussed in the literature. Medicine is a fundamental element for human health and drug transportation must be carried out on time and under conditions that will ensure that the drug does not lose its physical properties. Therefore, the pharmaceutical industry is one of the foremost and most important sectors in 3PL. In this multi-criteria problem where the evaluation criteria are linguistic rather than numerical, vagueness and impreciseness in evaluations can only be handled with the help of fuzzy sets. With the help of intuitionistic fuzzy sets, one of the new extensions of fuzzy sets, the vagueness and impreciseness here will be discussed and the 3PL supplier selection problem will be tried to be solved with the TOPSIS method, which is one of the most used MCDM methods in the literature. The use of Interval-Valued Intuitionistic Fuzzy sets will add more flexibility and accuracy to the assessment. Thus, the Interval-Valued Intuitionistic Fuzzy TOPSIS method is used to solve the 3PL supplier selection problem and the robustness of the decisions taken is tested with a sensitivity analysis.
Keywords: 3PL, Pharmaceutical sector, Interval-valued, Intuitionistic fuzzy sets, TOPSIS, Sensitivity analysis.
AMS Classification: 60-08, 62B10.
References:
  1. Aigbavboa, S., & Mbohwa, C. (2020). The headache of medicines’ supply in Nigeria: An exploratory study on the most critical challenges of pharmaceutical outbound value chains. Procedia Manufacturing, 43, 336–343.
  2. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20(1), 87–96.
  3. Atanassov K. T., & Gargov, G. (1989), Interval-Valued Fuzzy Sets, Fuzzy Sets and Systems, 31, 343–349.
  4. Benazzouz, T., Charkaoui, A., & Echchatbi, A. (2019). Risks related to the medical supplychain in public hospitals in Morocco: Qualitative study : Étude qualitative des risques lies a la chaîne logistique médicamenteuse au niveau des hôpitaux publics du Maroc. Pharmacien Hospitalier et Clinicien, 54(1), 19–29.
  5. Cebi, S., Gündoğdu, FK, Kahraman, C (2022). Operational risk analysis in business processes using decomposed fuzzy sets, Journal of Intelligent & Fuzzy Systems, 1–18.
  6. Cundell, T., Guilfoyle, D., Kreil, T. R., & Sawant, A. (2020). Controls to Minimize Disruption of the Pharmaceutical Supply Chain During the COVID-19 Pandemic. PDA Journal of Pharmaceutical Science and Technology, 74(4), 468–494.
  7. El Mokrini, A., & Aouam, T. (2020). A fuzzy multi-criteria decision analysis approach for risk evaluation in healthcare logistics outsourcing: Case of Morocco. Health Services Management Research, 33(3), 143–155.
  8. El Mokrini, A., Dafaoui, E., Berrado, A., & El Mhamedi, A. (2016). An approach to risk Assessment for Outsourcing Logistics: Case of Pharmaceutical Industry. IFACPapersOnLine, 49(12), 1239–1244.
  9. El Mokrini, A., Kafa, N., Dafaoui, E., El Mhamedi, A., & Berrado, A. (2016). Evaluating outsourcing risks in the pharmaceutical supply chain: Case of a multi-criteria combined fuzzy AHP-PROMETHEE approach. IFAC-PapersOnLine, 49(28), 114–119.
  10. Elleuch, H., Hachicha, W., & Chabchoub, H. (2013). A combined approach for supply chain risk management: Description and application to a real hospital pharmaceutical case study. Journal of Risk Research, 17(5), 641–663.
  11. Enyinda, C. I. (2018). Modeling enterprise risk management in operations and supply chain: A pharmaceutical firm context. Operations and Supply Chain Management, 11(1), 1–12.
  12. Enyinda, C. I., Briggs, C., & Bachkar, K. (2009). Managing risk in pharmaceutical global supply chain outsourcing: Applying analytic hierarchy process model. In: Proceedings of ABBS, 16(1), ASBBS Annual Conference: Las Vegas, February 2009.
  13. Franco, C., & Alfonso-Lizarazo, E. (2020). Optimization under uncertainty of the pharmaceutical supply chain in hospitals. Computers & Chemical Engineering, 135, 106689.
  14. Friemann, F., & Schönsleben, P. (2016). Reducing Global Supply Chain Risk Exposure of Pharmaceutical Companies by Further Incorporating Warehouse Capacity Planning into the Strategic Supply Chain Planning Process. Journal of Pharmaceutical Innovation, 11(2), 162–176.
  15. Ganguly, A., & Kumar, C. (2019). Evaluating supply chain resiliency strategies in the Indian pharmaceutical sector: A Fuzzy Analytic Hierarchy Process (F-AHP) approach. International Journal of the Analytic Hierarchy Process, 11(2), 153–180.
  16. Gómez, J. C. O., & España, K. T. (2020). Operational risk management in the pharma-ceutical supply chain using ontologies and fuzzy QFD. Procedia Manufacturing, 51, 1673–1679.
  17. Hatem, E., & Habib, C. (2011). Risks management in the downstream pharmaceutical supply chain: A study on the teaching hospital Habib Bourguiba Sfax. In: 4th International Conference on Logistics, Hammamet, Tunisia (31.05.2011 – 03.06.2011), 335–340.
  18. Jaberidoost, M., Olfat, L., Hosseini, A., Kebriaeezadeh, A., Abdollahi, M., Alaeddini, M., & Dinarvand, R. (2015). Pharmaceutical supply chain risk assessment in Iran using analytic hierarchy process (AHP) and simple additive weighting (SAW) methods. Journal of Pharmaceutical Policy and Practice, 8(1), 9.
  19. Jnandev Kamath, K., Krishnananda Kamath, K., Azaruddin, M., Subrahmanyam, E.V.S., & Shabharaya, A. R. (2012). Evaluation of different types of risks in pharmaceutical supply-chain. American Journal of PharmTech Research, 2(4), 280–287.
  20. Karmaker, C. L., & Ahmed, T. (2020). Modeling performance indicators of resilient pharmaceutical supply chain. Modern Supply Chain Research and Applications, 2(3), 179–205.
  21. Kumar, N., & Jha, A. (2018). Quality risk management during pharmaceutical ‘good distribution practices’ – A plausible solution. Bulletin of Faculty of Pharmacy, Cairo University, 56(1), 18–25.373
  22. Lawrence, J.-M., Ibne Hossain, N. U., Jaradat, R., & Hamilton, M. (2020). Leveraging a Bayesian network approach to model and analyze supplier vulnerability to severe weather risk: A case study of the U.S. pharmaceutical supply chain following Hurricane Maria. International Journal of Disaster Risk Reduction, 49, Article No. 101607.
  23. Li, M., Du, Y., Wang, Q., Sun, C., Ling, X., Yu, B., Tu, J., & Xiong, Y. (2016). Risk assessment of supply chain for pharmaceutical excipients with AHP-fuzzy comprehensive evaluation. Drug Development and Industrial Pharmacy, 42(4), 676–684.
  24. Lin, Q., Zhao, Q., & Lev, B. (2020). Cold chain transportation decision in the vaccine supply chain. European Journal of Operational Research, 283(1), 182–195.
  25. Lücker, F., & Seifert, R. W. (2017). Building up Resilience in a Pharmaceutical Supply Chain through Inventory, Dual Sourcing and Agility Capacity. Omega (United Kingdom), 73, 114–124.
  26. Marmolejo-Saucedo, J. A., Rodriguez-Aguilar, R., & Manuell-Barrera, O. S. G. (2019). Technical evaluation of the opening of facilities in the pharmaceutical industry: Optimization to supply chain in Mexico. IFAC-PapersOnLine, 52(13), 2692–2697.
  27. Mehralian, G., Rasekh, H. R., Akhavan, P., & Farzandy, G. (2012). An assessment of structural (organizational) and relational capital indicators in knowledge intensive industries: Evidence from pharmaceutical industry. Journal of Basic and Applied Scientific Research, 2(8), 8240–8248.
  28. Moktadir, M. A., Ali, S. M., Mangla, S. K., Sharmy, T. A., Luthra, S., Mishra, N., & GarzaReyes, J. A. (2018). Decision modeling of risks in pharmaceutical supply chains. Industrial Management and Data Systems, 118(7), 1388–1412.
  29. Nasrollahi, M., & Razmi, J. (2019). A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty. Operational Research, 21(1), 525–552.
  30. Nsamzinshuti, A., Cardoso, F., Janjevic, M., & Ndiaye, A. B. (2017). Pharmaceutical distribution in urban area: An integrated analysis and perspective of the case of BrusselsCapital Region (BRC). Transportation Research Procedia, 25, 747–761.
  31. Ouabouch, L., & Amri, M. (2013). Analysing Supply Chain Risk Factors: A probability-Impact Matrix Applied to Pharmaceutical Industry. Journal of Logistics Management, 2(2), 35–40.
  32. Paul, S., Kabir, G., Ali, S. M., & Zhang, G. (2020). Examining transportation disruption risk in supply chains: A case study from Bangladeshi pharmaceutical industry. Research in Transportation Business & Management, 37, 100485.
  33. Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657–672.
  34. Sampat, A. M., Kumar, R., Pushpangatha Kurup, R., Chiu, K., Saucedo, V. M., & Zavala, V. M. (2021). Multisite supply planning for drug products under uncertainty. AIChE Journal, 67(1), Article No. e17069.374
  35. Silva, J., Araujo, C., & Marques, L. (2020). Siloed Perceptions in Pharmaceutical Supply Chain Risk Management: A Brazilian Perspective. Latin American Business Review, 21(3), 223–254.
  36. Torasa, C., & Mekhum, W. (2020). Supply chain risk factors and corporate repute in pharma industry of Thailand. Systematic Reviews in Pharmacy, 11(4), 94–101.
  37. Türk, H., & Güner, S. (2021). A Field Study on the Pharmaceutical Supply Chain Structure and Practices in Turkey. Journal of Transportation and Logistics, 6 (2), 177–196.
  38. Vishwakarma, V., Garg, C. P., & Barua, M. K. (2019). Modelling the barriers of Indian pharmaceutical supply chain using fuzzy AHP. International Journal of Operational Research, 34(2), 240–268.
  39. Vishwakarma, V., Prakash, C., & Barua, M. K. (2016). A fuzzy-based multi criteria decision making approach for supply chain risk assessment in Indian pharmaceutical industry. International Journal of Logistics Systems and Management, 25(2), 245–265.
  40. Wang, M., & Jie, F. (2020). Managing supply chain uncertainty and risk in the pharmaceutical industry. Health Services Management Research, 33(3), 156–164.
  41. Yalcinkaya, I., & Cebi, S. (2022). Using Fuzzy Set Based Model for Pharmaceutical Supply Chain Risks Assessment. In: International Conference on Intelligent and Fuzzy Systems (INFUS 2022), Izmir, Turkey (19.07.2022–21.07.2022). Lecture Notes in Networks and Systems, Vol. 504, 252–260.
  42. Ye, F. (2010). An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection. Expert Systems with Applications, 37(10), 7050–7055.
  43. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
Citations:

The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.

Retrieved from "https://ifigenia.org/index.php?title=Issue:Pharmaceutical_3PL_supplier_selection_using_interval-valued_intuitionistic_fuzzy_TOPSIS&oldid=11977"