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# 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.
# 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.
# Franco, C., & Alfonso-Lizarazo, E. (2020). Optimization under uncertainty of the pharmaceutical supply chain in hospitals. Computers & Chemical Engineering, 135, 106689.
# Franco, C., & Alfonso-Lizarazo, E. (2020). Optimization under uncertainty of the pharmaceutical supply chain in hospitals. Computers & Chemical Engineering, 135, 106689.
# 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),
# 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.
162–176.
# 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.
# 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.
# 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.
# 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.
Line 72: Line 71:
# 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.
# 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.
# 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.
# 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.
# 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
# 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.
Research, 2(8), 8240–8248.
# 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.
# 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.
# 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.
# 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.
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# 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.
# 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.
# Wang, M., & Jie, F. (2020). Managing supply chain uncertainty and risk in the pharmaceutical industry. Health Services Management Research, 33(3), 156–164.
# Wang, M., & Jie, F. (2020). Managing supply chain uncertainty and risk in the pharmaceutical industry. Health Services Management Research, 33(3), 156–164.
# 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
# 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.
Systems, Vol. 504, 252–260.
# 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.
# 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.
# Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
# Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

Latest revision as of 03:46, 9 September 2022

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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
Selcuk Cebi
Department of Industrial Engineering, Istanbul Technical University, Besiktas, Istanbul, Türkiye
scebi@itu.edu.tr
Sezi Cevik 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, 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.
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