[1] Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European journal of operational research, 227(1), 199–215. https://doi.org/10.1016/j.ejor.2012.12.017
[2] Edalatpanah, S. A., Komazec, N., & Pamucar, D. (2024). Big data and computing visions, 4(3), 201–218. https://julista.sinaweb.net/article_207199_350289d4d91b8f78a445231a6074c47c.pdf
[3] Montazeri, F. Z., & Sorourkhah, A. (2024). Robust-fuzzy-probabilistic optimization for a resilient, sustainable supply chain with an inventory management approach by the seller. Big data and computing visions, 4(2), 146–163. https://doi.org/10.22105/bdcv.2024.481945.1208
[4] Ikpe, A. E. (2024). thermodynamic analysis and energy balance for environmental sustainability of simple gas turbine inlet cooling system in afam power plant, Computational algorithms and numerical dimensions, 3(1), 79–93. https://doi.org/10.22105/cand.2024.463423.1097
[5] Aliu, O. R. (2025). How realistic is low carbon development for developing countries that is development without significant exploitation of fossil fuels ?,Systemic analytics, 3(1), 1–7. https://doi.org/10.31181/sa31202536
[6] Purvis, B., Mao, Y., & Robinson, D. (2019). Three pillars of sustainability: in search of conceptual origins. Sustainability science, 14, 681-695. https://doi.org/10.1007/s11625-018-0627-5
[7] Lotfi, R., Weber, G. W., & Tirkolaee, E. B. (2023). Recent advances in viable and sustainable supply chain management. Environmental science and pollution research, 30(39), 89943–89944. https://doi.org/10.1007/s11356-023-28810-4
[8] Rabbani, M., Hosseini-Mokhallesun, S. A. A., Ordibazar, A. H., & Farrokhi-Asl, H. (2020). A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design. International journal of systems science: operations & logistics, 7(1), 60–75. https://doi.org/10.1080/23302674.2018.1506061
[9] Nayeri, S., Zhain, V. M., Asadi, Z., Sazvar, Z., & Javadian, N. (2024). Raw material provider selection problem considering the digitalization, circular economy and resilience dimensions: a case study. International journal of industrial engineering & production research, 35(3), 1-20. https://doi.org/10.22068/ijiepr.35.3.1997
[10] Babaei, Y. S., Sazvar, Z., Nayeri, S., & Tavakkoli-Moghaddam, R. (2024). A two-stage framework for a resilient medical tourism supply chain considering social aspects and supplier evaluation under uncertainty: A real-case study. Annals of operations research, 1-47. https://doi.org/10.1007/s10479-024-06128-9
[11] Silva, A. C., Marques, C. M., & de Sousa, J. P. (2023). A simulation approach for the design of more sustainable and resilient supply chains in the pharmaceutical industry. Sustainability, 15(9), 7254. https://doi.org/10.3390/su15097254
[12] Nayeri, S., Sazvar, Z., & Heydari, J. (2022). A global-responsive supply chain considering sustainability and resiliency: Application in the medical devices industry. Socio-economic planning sciences, 82, 101303. https://doi.org/10.1016/j.seps.2022.101303
[13] Subramanian, B., Mishra, A., Ramkumar, B. V., Mandala, G., Kathamuthu, N. D., & Srithar, S. (2025). Big data and fuzzy logic for demand forecasting in supply chain management: a data-driven approach. Journal of fuzzy extension and applications, 6(2), 260–283. https://doi.org/10.22105/jfea.2025.488816.1703
[14] Hoseini, S. A., Hashemkhani Zolfani, S., Skačkauskas, P., Fallahpour, A., & Saberi, S. (2021). A combined interval type-2 fuzzy MCDM framework for the resilient supplier selection problem. Mathematics, 10(1), 44. https://www.mdpi.com/2227-7390/10/1/44
[15] Fallahpour, A., Olugu, E. U., Musa, S. N., Wong, K. Y., & Noori, S. (2017). A decision support model for sustainable supplier selection in sustainable supply chain management. Computers & industrial engineering, 105, 391-410. https://doi.org/10.1016/j.cie.2017.01.005
[16] Mohammed, A., Bai, C., Channouf, N., Ahmed, T. Al, & Mohamed, S. M. (2023). G-resilient multi-tier supplier selection and order allocation in food industry: a hybrid methodology. International journal of systems science: operations and logistics, 10(1). https://doi.org/10.1080/23302674.2023.2195055
[17] Govindan, K., Mina, H., Esmaeili, A., & Gholami-Zanjani, S. M. (2020). An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of cleaner production, 242. https://doi.org/10.1016/j.jclepro.2019.118317
[18] Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & Banaitis, A. (2020). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International journal of sustainable development and world ecology. https://doi.org/10.1080/13504509.2020.1793424
[19] Ramezankhani, M. J., Torabi, S. A., & Vahidi, F. (2018). Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach. Computers & industrial engineering, 126, 531-548. https://doi.org/10.1016/j.cie.2018.09.054
[20] Aldrighetti, R., Battini, D., Ivanov, D., & Zennaro, I. (2021). Costs of resilience and disruptions in supply chain network design models: a review and future research directions. International Journal of Production Economics, 235, 108103. https://doi.org/10.1016/j.ijpe.2021.108103
[21] Elham Moghimi, Adel Az, & Mahmoud Dehghan Nair. (2024). Modeling key performance indicators and resilience elements with a structural equation modeling approach. Innovation management & operational strategies , 5(4), 381-401. (In Presion). https://search.ebscohost.com
[22] Alavi, B., Tavana, M., & Mina, H. (2021). A dynamic decision support system for sustainable supplier selection in circular economy. Sustainable production and consumption, 27, 905–920. https://doi.org/10.1016/j.spc.2021.02.015
[23] Wu, C., Lin, Y., & Barnes, D. (2021). An integrated decision-making approach for sustainable supplier selection in the chemical industry. Expert systems with applications, 184, 115553. https://doi.org/10.1016/j.eswa.2021.115553
[24] Homayouni, Z., Pishvaee, M. S., Jahani, H., & Ivanov, D. (2023). A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty. Annals of operations research, 1-41. https://doi.org/10.1007/s10479-021-03985-6
[25] Burke, H., Zhang, A., & Wang, J. X. (2023). Integrating product design and supply chain management for a circular economy. Production planning & control, 34(11), 1097–1113. https://doi.org/10.1080/09537287.2021.1983063
[26] Deliktaş, D., Karagoz, S., Simić, V., & Aydin, N. (2023). A stochastic Fermatean fuzzy-based multi-choice conic goal programming approach for sustainable supply chain management in end-of-life buildings. Journal of cleaner production, 382, 135305. https://doi.org/10.1016/j.jclepro.2022.135305
[27] Zhen, L., Huang, L., & Wang, W. (2019). Green and sustainable closed-loop supply chain network design under uncertainty. Journal of cleaner production, 227, 1195–1209. https://doi.org/10.1016/j.jclepro.2019.04.098
[28] Jiang, M., Jia, F., Chen, L., & Xing, X. (2024). Technology adoption in socially sustainable supply chain management: Towards an integrated conceptual framework. Technological forecasting and social change, 206, 123537. https://doi.org/10.1016/j.techfore.2024.123537
[29] Asadi, Z., Khatir, M. V., & Rahimi, M. (2022). Robust design of a green-responsive closed-loop supply chain network for the ventilator device. Environmental science and pollution research, 1–21. https://doi.org/10.1007/s11356-022-19105-1
[30] Mastos, T. D., Gotzamani, K., Ieromonachou, P., & Tsiotras, G. (2024). A structural equation model for sustainable supply chain management in the food industry. Foods, 13(23), 3713. https://www.mdpi.com/2304-8158/13/23/3713
[31] Habib, M. S., Omair, M., Ramzan, M. B., Chaudhary, T. N., Farooq, M., & Sarkar, B. (2022). A robust possibilistic flexible programming approach toward a resilient and cost-efficient biodiesel supply chain network. Journal of cleaner production, 366, 132752. https://doi.org/10.1016/j.jclepro.2022.132752
[32] Leong, W. Y., Wong, K. Y., & Wong, W. P. (2022). A new integrated multi-criteria decision-making model for resilient supplier selection. Applied system innovation, 5(1), 8. https://www.mdpi.com/2571-5577/5/1/8
[33] Goldbeck, N., Angeloudis, P., & Ochieng, W. (2020). Optimal supply chain resilience with consideration of failure propagation and repair logistics. Transportation research part e: logistics and transportation review, 133, 101830. https://doi.org/10.1016/j.tre.2019.101830
[34] Uzoma Okwudili Nnaji, Lucky Bamidele Benjamin, Nsisong Louis Eyo-Udo, & Emmanuel Augustine Etukudoh. (2024). Strategies for enhancing global supply chain resilience to climate change. International journal of management & entrepreneurship research, 6(5), 1677–1686. https://doi.org/10.51594/ijmer.v6i5.1141
[35] Cinti, A., Marcone, M. R., Sabatini, A., & Temperini, V. (2025). Enhancing supply chain resilience through the supply network approach. Journal of business & industrial marketing, 40(4), 858-876. https://www.emerald.com/insight/content/doi/10.1108/jbim-02-2023-0106/full/html
[36] Govindan, K., & Gholizadeh, H. (2021). Robust network design for sustainable-resilient reverse logistics network using big data: A case study of end-of-life vehicles. Transportation research part e: logistics and transportation review, 149, 102279. https://doi.org/10.1016/j.tre.2021.102279
[37] Fallahpour, A., Nayeri, S., Sheikhalishahi, M., Wong, K. Y., Tian, G., & Fathollahi-Fard, A. M. (2021). A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian Palm oil industry. Environmental science and pollution research, 1–21. https://doi.org/10.1007/s11356-021-12491-y
[38] Bayatzadeh, S., & Talaie, H. (2024). Identifying and Evaluating of Sustainable and Resilient Supplier Selection Criteria According to Industry 5.0 Concepts (Case Study: Steel Industry). Tasmimgiri va tahqiq dar amaliyyat, 9(4), 1045–1063. https://doi.org/10.22105/dmor.2025.498734.1904
[39] Zeinab Asadi, Hassanali Aghajani, Mohammad Valipour Khatir, & Erfan Babai Ghadikalai. (2024). Presenting a fuzzy mixed decision-making approach to evaluate suppliers based on the dimensions of the fifth industrial revolution and sustainable supply chain: A case study of medical equipment. Journal of decisions & operations research ,9(2), 399-418. (In Presion). https://search.ebscohost.com
[40] Shabbir, M. S., Mahmood, A., Setiawan, R., Nasirin, C., Rusdiyanto, R., Gazali, G., ... & Batool, F. (2021). Closed-loop supply chain network design with sustainability and resiliency criteria. Environmental science and pollution research, 1-16. https://doi.org/10.1007/s11356-021-12980-0
[41] Foroozesh, N., Karimi, B., Mousavi, S. M., & Mojtahedi, M. (2023). A hybrid decision-making method using robust programming and interval-valued fuzzy sets for sustainable-resilient supply chain network design considering circular economy and technology levels. Journal of industrial information integration, 33. https://doi.org/10.1016/j.jii.2023.100440
[42] Vali-Siar, M. M., & Roghanian, E. (2022). Sustainable, resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption. Sustainable production and consumption, 30, 278-300. https://doi.org/10.1016/j.spc.2021.12.003
[43] Kayani, S. A., Warsi, S. S., & Liaqait, R. A. (2023). A smart decision support framework for sustainable and resilient supplier selection and order allocation in the pharmaceutical industry. Sustainability (switzerland). https://doi.org/10.3390/su15075962
[44] Agarwal, R., & Nishad, A. K. (2024). A Fuzzy Mathematical Modeling for Evaluation and Selection of a Best Sustainable and Resilient Supplier by Using EDAS Technique. Process integration and optimization for sustainability, 8(1), 71–80. https://doi.org/10.1007/s41660-023-00352-9
[45] Chaouni Benabdellah, G., Bennis, K., Chaouni Benabdellah, A., & Zekhnini, K. (2022). Resilient sustainable supplier selection criteria assessment for economics enhancement in industry 4.0 context. IFIP advances in information and communication technology (Vol. 639 IFIP, pp. 194–208). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-94335-6_14
[46] Koç, S., Erden, C., Ateş, Ç., & Ceviz, E. (2024). Evaluation of potential logistics village alternatives using bayesian best-worst method. Optimality, 1(1), 100-120. https://doi.org/10.22105/opt.v1i1.35
[47] Karimi, H., Sadeghi-Dastaki, M., & Javan, M. (2020). A fully fuzzy best–worst multi attribute decision making method with triangular fuzzy number: A case study of maintenance assessment in the hospitals. Applied soft computing, 86, 105882. https://doi.org/10.1016/j.asoc.2019.105882
[48] Sazvar, Z., Tavakoli, M., Ghanavati-Nejad, M., & Nayeri, S. (2022). Sustainable-resilient supplier evaluation for high-consumption drugs during COVID-19 pandemic using a data-driven decision-making approach. Scientia iranica. https://doi.org/10.24200/sci.2022.59789.6424
[49] Nayeri, S., Sazvar, Z., & Heydari, J. (2023). Designing an IoT-enabled supply chain network considering the perspective of the Fifth Industrial Revolution: Application in the medical devices industry. Engineering applications of artificial intelligence, 122, 106113. https://doi.org/10.1016/j.engappai.2023.106113
[50] Nayeri, S., Sazvar, Z., & Heydari, J. (2023). Towards a responsive supply chain based on the industry 5.0 dimensions: A novel decision-making method. Expert systems with applications, 213, 119267. https://doi.org/10.1016/j.eswa.2022.119267
[51] Shakerian Hagigi, S., Nezhad Heydari, Z., Raeiszadeh, M., & Qayumi, M. (2023). Selection of the best contractor by AHP and VIKOR method, a case study of an executive project in the dairy industry. Modern research in performance evaluation, 2(2), 79-90. (In Presion). https://www.journal-mrpe.ir/article_181692_ea3eab244553df51ff2f24a4150fb984.pdf
[52] Nayeri, S., Torabi, S. A., Tavakoli, M., & Sazvar, Z. (2021). A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network. Journal of cleaner production, 311, 127691. https://doi.org/10.1016/j.jclepro.2021.127691
[53] Alamroshan, F., La’li, M., & Yahyaei, M. (2022). The green-agile supplier selection problem for the medical devices: a hybrid fuzzy decision-making approach. Environmental science and pollution research, 29(5), 6793–6811. https://doi.org/10.1007/s11356-021-14690-z
[54] Solgi, O., Gheidar-Kheljani, J., Dehghani, E., & Taromi, A. (2021). Resilient supplier selection in complex products and their subsystem supply chains under uncertainty and risk disruption: A case study for satellite components. Scientia iranica. https://doi.org/10.24200/SCI.2019.52556.2773
[55] Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation research part e: logistics and transportation review, 103, 109–142. https://doi.org/10.1016/j.tre.2017.04.009
[56] Taheri, F., & Moghaddam, B. F. (2022). A heuristic-based hybrid algorithm to configure a sustainable supply chain network for medical devices considering information-sharing systems. Environmental science and pollution research, 29(60), 91105-91126. https://doi.org/10.1007/s11356-022-22147-0
[57] Gholizadeh, H., Fazlollahtabar, H., & Khalilzadeh, M. (2020). A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data. Journal of cleaner production, 258, 120640.
[58] Nayeri, S., Paydar, M. M., Asadi-Gangraj, E., & Emami, S. (2020). Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design. Computers & industrial engineering, 148, 106716. https://doi.org/10.1016/j.cie.2020.106716
[59] Gütmen, S., Roy, S. K., & Weber, G. W. (2024). An overview of weighted goal programming: a multi-objective transportation problem with some fresh viewpoints. Central european journal of operations research, 32(3), 557-568. https://doi.org/10.1007/s10100-023-00861-5
[60] Nayeri, S., Tavakoli, M., Tanhaeean, M., & Jolai, F. (2022). A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms. Annals of operations research, 1-41. https://doi.org/10.1007/s10479-021-03977-6
[61] Tirkolaee, E. B., Mardani, A., Dashtian, Z., Soltani, M., & Weber, G.-W. (2020). A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. Journal of cleaner production, 250, 119517. https://doi.org/10.1016/j.jclepro.2019.119517
[62] Rostami, O., Tavakoli, M., Tajally, A. R., & GhanavatiNejad, M. (2023). A goal programming-based fuzzy best–worst method for the viable supplier selection problem: a case study. Soft computing, 27(6), 2827–2852. https://doi.org/10.1007/s00500-022-07572-0
[63] Mamashli, Z., Nayeri, S., Tavakkoli-Moghaddam, R., Sazvar, Z., & Javadian, N. (2021). Designing a sustainable–resilient disaster waste management system under hybrid uncertainty: A case study. Engineering applications of artificial intelligence, 106, 104459. https://doi.org/10.1016/j.engappai.2021.104459
