2836-7863ISSN (Online)
Volume 1 , Issue 1 , PP: 27-33, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Abduallah Gamal 1 * , Nehal Nabil Mostafa 2
Doi: https://doi.org/10.54216/NIF.010103
Because of stricter rules from the government and growing awareness among the general public, sustainable supply chain management (SSCM) performs a significant role in the management of firm manufacturing operations. Companies that want to promote sustainable supply chain management (SSCM) must first choose the most suitable sustainable supplier, which is a MCDM dilemma, as highlighted in a number of research studies. In addition, because of their limited expertise, those who make decisions have a propensity to convey their opinions via the use of language phrases. The purpose of this work is to report on a unique MCDM model for the choice of sustainable suppliers. This approach integrates the MCDM MABAC method inside an uncertain language situation. With the assistance of uncertain linguistic sets, the neutrosophic sets used to overcome these uncertainty. When it comes to generating the ranking of possible suppliers, the MABAC is dependable and easy to understand. In conclusion, an iron maker is used as an example to illustrate the practicability and efficacy of the suggested strategy for the selection of sustainable suppliers.
Sustainable Supplier Selection , Decision Making , Neutrosophic Sets , MCDM , MABAC
[1] J. J. Jassbi, R. A. Ribeiro, and L. R. Varela, “Dynamic MCDM with future knowledge for supplier selection,” Journal of Decision Systems, vol. 23, no. 3, pp. 232–248, 2014.
[2] M. Ghorbani, S. Mohammad Arabzad, and A. Shahin, “A novel approach for supplier selection based on the Kano model and fuzzy MCDM,” International Journal of Production Research, vol. 51, no. 18, pp. 5469–5484, 2013.
[3] S. Hamdan and A. Cheaitou, “Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach,” Computers & Operations Research, vol. 81, pp. 282–304, 2017.
[4] G. Büyüközkan and F. Göçer, “Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem,” Applied Soft Computing, vol. 52, pp. 1222–1238, 2017.
[5] Ž. Stević, D. Pamučar, A. Puška, and P. Chatterjee, “Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS),” Computers & Industrial Engineering, vol. 140, p. 106231, 2020.
[6] B. Matić et al., “A new hybrid MCDM model: Sustainable supplier selection in a construction company,” Symmetry, vol. 11, no. 3, p. 353, 2019.
[7] M. Yazdani, P. Chatterjee, E. K. Zavadskas, and S. H. Zolfani, “Integrated QFD-MCDM framework for green supplier selection,” Journal of Cleaner Production, vol. 142, pp. 3728–3740, 2017.
[8] S. Önüt, S. S. Kara, and E. Işik, “Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company,” Expert systems with applications, vol. 36, no. 2, pp. 3887–3895, 2009.
[9] M. Yazdani, “An integrated MCDM approach to green supplier selection,” International Journal of Industrial Engineering Computations, vol. 5, no. 3, pp. 443–458, 2014.
[10] K. Renganath and M. Suresh, “Supplier selection using fuzzy MCDM techniques: A literature review,” in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2016, pp. 1–6.
[11] A. Haldar, A. Ray, D. Banerjee, and S. Ghosh, “A hybrid MCDM model for resilient supplier selection,” International Journal of Management Science and Engineering Management, vol. 7, no. 4, pp. 284–292, 2012.
[12] G. Petrović, J. Mihajlović, Ž. Ćojbašić, M. Madić, and D. Marinković, “Comparison of three fuzzy MCDM methods for solving the supplier selection problem,” Facta Universitatis, Series: Mechanical Engineering, vol. 17, no. 3, pp. 455–469, 2019.
[13] Y. Deng and F. T. S. Chan, “A new fuzzy dempster MCDM method and its application in supplier selection,” Expert Systems with Applications, vol. 38, no. 8, pp. 9854–9861, 2011.
[14] M. Dursun and E. E. Karsak, “A QFD-based fuzzy MCDM approach for supplier selection,” Applied Mathematical Modelling, vol. 37, no. 8, pp. 5864–5875, 2013.
[15] P. Shojaei and A. Bolvardizadeh, “Rough MCDM model for green supplier selection in Iran: a case of university construction project,” Built Environment Project and Asset Management, vol. 10, no. 3, pp. 437–452, 2020.
[16] N. Jain, A. R. Singh, and R. K. Upadhyay, “Sustainable supplier selection under attractive criteria through FIS and integrated fuzzy MCDM techniques,” International Journal of Sustainable Engineering, vol. 13, no. 6, pp. 441–462, 2020.
[17] M.-Y. Quan, Z.-L. Wang, H.-C. Liu, and H. Shi, “A hybrid MCDM approach for large group green supplier selection with uncertain linguistic information,” IEEE Access, vol. 6, pp. 50372–50383, 2018.
[18] M. Rezaeisaray, S. Ebrahimnejad, and K. Khalili-Damghani, “A novel hybrid MCDM approach for outsourcing supplier selection: A case study in pipe and fittings manufacturing,” Journal of Modelling in Management, vol. 11, no. 2, pp. 536–559, 2016.
[19] J.-C. Kao, C.-N. Wang, V. T. Nguyen, and S. T. Husain, “A Fuzzy MCDM Model of Supplier Selection in Supply Chain Management.,” Intelligent Automation & Soft Computing, vol. 31, no. 3, 2022.
[20] V. B. Schramm, L. P. B. Cabral, and F. Schramm, “Approaches for supporting sustainable supplier selection-A literature review,” Journal of cleaner production, vol. 273, p. 123089, 2020.
[21] A. Rezaei, M. Rahiminezhad Galankashi, S. Mansoorzadeh, and F. Mokhatab Rafiei, “Supplier selection and order allocation with lean manufacturing criteria: An integrated MCDM and bi-objective modelling approach,” Engineering Management Journal, vol. 32, no. 4, pp. 253–271, 2020.
[22] D. Pamučar and D. Božanić, Selection of a location for the development of multimodal logistics center: Application of single-valued neutrosophic MABAC model. Infinite Study, 2019.
[23] X. Peng and J. Dai, “Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function,” Neural Computing and Applications, vol. 29, no. 10, pp. 939–954, 2018.
[24] P. Ji, H. Zhang, and J. Wang, “Selecting an outsourcing provider based on the combined MABAC–ELECTRE method using single-valued neutrosophic linguistic sets,” Computers & Industrial Engineering, vol. 120, pp. 429–441, 2018.
[25] R. Şahin and F. Altun, “Decision making with MABAC method under probabilistic single-valued neutrosophic hesitant fuzzy environment,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 10, pp. 4195–4212, 2020.
[26] P. Liu and S. Cheng, “An improved MABAC group decision-making method using regret theory and likelihood in probability multi-valued neutrosophic sets,” International Journal of Information Technology & Decision Making, vol. 19, no. 05, pp. 1353–1387, 2020.
