Evaluation of sustainable third-party reverse logistics providers using a Fermatean fuzzy rough number-based decision-making framework

[1] A. Aguezzoul, Third-party logistics selection problem: A literature review on criteria and methods, Omega, 49
(2014), 69-78. https://doi.org/10.1016/j.omega.2014.05.009

[2] Z. Akram, U. Ahmad, J. C. R. Alcantud, Multi-criteria decision-making for the selection of best airport ground
access mode with a new fuzzy rough-entropy based method, Engineering Applications of Artificial Intelligence, 135
(2024), 108843. https://doi.org/10.1016/j.engappai.2024.108843

[3] M. Akram, M. Sultan, C. Kahraman, An extended outranking technique based on spherical fuzzy rough numbers for
circular economy business models in small and medium-sized enterprises, Applied Soft Computing, (2024), 112496.
https://doi.org/10.1016/j.asoc.2024.112496

[4] M. Akram, S. Zahid, A. N. Al-Kenani, Multi-criteria group decision-making for evaluating efficient and smart
mobility sharing systems using Pythagorean fuzzy rough numbers, Granular Computing, 9(2) (2024), 50. https:
//doi.org/10.1007/s41066-024-00466-6

[5] C. Bai, J. Sarkis, Integrating and extending data and decision tools for sustainable third-party reverse logistics
provider selection, Computers and Operations Research, 110 (2019), 188-207. https://doi.org/10.1016/j.cor.
2018.06.005

[6] W. K. M. Brauers, E. K. Zavadskas, Project management by MULTIMOORA as an instrument for transition
economies, Technological and Economic Development of Economy, 16(1) (2010), 5-24.

[7] G. B¨uy¨uk¨ozkan, D. Uzt¨urk, ¨ O. Ilicak, Fermatean fuzzy sets and its extensions: A systematic literature review,
Artificial Intelligence Review, 57(6) (2024), 138. https://doi.org/10.1007/s10462-024-10761-y

[8] Z. S. Chen, X. Zhang, K. Govindan, X. J. Wang, K. S. Chin, Third-party reverse logistics provider selection: A
computational semantic analysis-based multi-perspective multi-attribute decision-making approach, Expert Systems
with Applications, 166 (2021), 114051. https://doi.org/10.1016/j.eswa.2020.114051

[9] A. Eydi, S. Rastgar, A DEA model with dual-role factors and fuzzy data for selecting third-party reverse logistics
provider, case study: Hospital waste collection, Ain Shams Engineering Journal, 13(2) (2022), 101561. https:
//doi.org/10.1016/j.asej.2021.07.011

[10] K. Govindan, V. Agarwal, J. D. Darbari, P. C. Jha, An integrated decision making model for the selection of
sustainable forward and reverse logistic providers, Annals of Operations Research, 273 (2019), 607-650. https:
//doi.org/10.1007/s10479-017-2654-5

[11] K. Govindan, M. Kadzinski, R. Ehling, G. Miebs, Selection of a sustainable third-party reverse logistics provider
based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA, Omega, 85
(2019), 1-15. https://doi.org/10.1016/j.omega.2018.05.007

[12] A. Ijadi Maghsoodi, G. Abouhamzeh, M. Khalilzadeh, E. K. Zavadskas, Ranking and selecting the best performance
appraisal method using the MULTIMOORA approach integrated Shannon’s entropy, Frontiers of Business Research
in China, 12 (2018), 1-21. https://doi.org/10.1186/s11782-017-0022-6/tables/9

[13] A. Jayant, S. Singh, T. Walke, A robust hybrid multi-criteria decision-making approach for selection of thirdparty
reverse logistics service provider, In Advances in Production and Industrial Engineering, (2021), 423-443.
https://doi.org/10.1007/978-981-15-5519-0_32

[14] Y. L. Li, C. S. Ying, K. S. Chin, H. T. Yang, J. Xu, Third-party reverse logistics provider selection approach based
on hybrid-information MCDM and cumulative prospect theory, Journal of Cleaner Production, 195 (2018), 573-584.
https://doi.org/10.1016/j.jclepro.2018.05.213

[15] A. Liu, X. Ji, H. Lu, H. Liu, The selection of 3PRLs on self-service mobile recycling machine: Interval-valued
Pythagorean hesitant fuzzy best-worst multi-criteria group deciion-making, Journal of Cleaner Production, 230
(2019), 734-750. https://doi.org/10.1016/j.jclepro.2019.04.257

[16] A. Mohammadkhani, S. M. Mousavi, A new last aggregation fuzzy compromise solution approach for evaluating
sustainable third-party reverse logistics providers with an application to food industry, Expert Systems with Applications,
216 (2023), 119396. https://doi.org/10.1016/j.eswa.2022.119396

[17] D. Pamuˇcar, K. Chatterjee, E. K. Zavadskas, Assessment of third-party logistics provider using multi-criteria
decision-making approach based on interval rough numbers, Computers and Industrial Engineering, 127 (2019),
383-407. https://doi.org/10.1016/j.cie.2018.10.023

[18] C. Prakash, M. K. Barua, An analysis of integrated robust hybrid model for third-party reverse logistics partner
selection under fuzzy environment, Resources, Conservation and Recycling, 108 (2016), 63-81. https://doi.org/
10.1016/j.resconrec.2015.12.011

[19] C. Prakash, M. K. Barua, A combined MCDM approach for evaluation and selection of third-party reverse logistics
partner for Indian electronics industry, Sustainable Production and Consumption, 7 (2016), 66-78. https://doi.
org/10.1016/j.spc.2016.04.001

[20] M. Sarwar, M. Akram, W. Gulzar, M. Deveci, Group decision making method for third-party logistics management:
An interval rough cloud optimization model, Journal of Industrial Information Integration, 41 (2024), 100658.
https://doi.org/10.1016/j.jii.2024.100658

[21] T. Senapati, R. R. Yager, Fermatean fuzzy sets, Journal of Ambient Intelligence and Humanized Computing, 11
(2020), 663-674. https://doi.org/10.1007/s12652-019-01377-0

[22] S. Senthil, B. Srirangacharyulu, A. Ramesh, A robust hybrid multi-criteria decision making methodology for contractor
evaluation and selection in third-party reverse logistics, Expert Systems with Applications, 41(1) (2014),
50-58. https://doi.org/10.1016/j.eswa.2013.07.010

[23] Z. Shang, X. Yang, D. Barnes, C. Wu, Supplier selection in sustainable supply chains: Using the integrated BWM,
fuzzy Shannon entropy, and fuzzy MULTIMOORA methods, Expert Systems with Applications, 195 (2022), 116567.
https://doi.org/10.1016/j.eswa.2022.116567

[24] C. E. Shannon, A mathematical theory of communication, The Bell System Technical Journal, 27(3) (1948), 379-
423.

[25] R. Wang, X. Li, C. Li, Optimal selection of sustainable battery supplier for battery swapping station based on
Triangular fuzzy entropy-MULTIMOORA method, Journal of Energy Storage, 34 (2021), 102013. https://doi.
org/10.1016/j.est.2020.102013

[26] C. Yang, Q. Wang, M. Pan, J. Hu, W. Peng, J. Zhang, L. Zhang, A linguistic Pythagorean hesitant fuzzy MULTIMOORA
method for third-party reverse logistics provider selection of electric vehicle power battery recycling, Expert
Systems with Applications, 198 (2022), 116808. https://doi.org/10.1016/j.eswa.2022.116808

[27] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning-I, Information
Sciences, 8(3) (1975), 199-249. https://doi.org/10.1016/0020-0255(75)90036-5

[28] N. Zarbakhshnia, H. Soleimani, H. Ghaderi, Sustainable third-party reverse logistics provider evaluation and selection
using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria, Applied Soft Computing,
65 (2018), 307-319. https://doi.org/10.1016/j.asoc.2018.01.023

[29] N. Zarbakhshnia, Y. Wu, K. Govindan, H. Soleimani, A novel hybrid multiple attribute decision-making approach
for outsourcing sustainable reverse logistics, Journal of Cleaner Production, 242 (2020), 118461. https://doi.org/
10.1016/j.jclepro.2019.118461

[30] L. Y. Zhai, L. P. Khoo, Z. W. Zhong, A rough set enhanced fuzzy approach to quality function deployment, The
International Journal of Advanced Manufacturing Technology, 37 (2008), 613-624. https://doi.org/10.1007/
s00170-007-0989-9

[31] G. N. Zhu, J. Hu, H. Ren, A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain
environments, Applied Soft Computing, 91 (2020), 106228. https://doi.org/10.1016/j.asoc.2020.106228