Journal of Quality Engineering and Management

Optimization of a closed-loop viable supply chain network under hybrid uncertainty

Document Type : Original Article

Authors

Fariborz Kalashi
Iraj Mahdavi
Ali Tajdin
Javad Rezaeian

Department of Industrial Engineering, Mazandaran University of Science and Technology, Mazandaran, Iran.

https://doi.org/10.48313/jqem.2026.564808.1595
Abstract
Purpose: This study aims to develop a durable closed-loop supply chain network capable of simultaneously addressing sustainability, resilience, agility, and digitalization while incorporating fuzzy–stochastic uncertainties. The significance of this research lies in the limitations of traditional supply chains, which often fail to perform effectively under severe environmental fluctuations, operational disruptions, and demand variability, thereby highlighting the need for intelligent and multidimensional decision-making frameworks.
Methodology: To achieve the research objectives, a structured three-phase framework was designed. In the first phase, demand subject to considerable uncertainty was forecast using the SARIMA time-series model to capture market volatility and seasonal patterns. In the second phase, supplier evaluation criteria were identified through a systematic literature review and expert judgment, and subsequently weighted via the Stochastic–Fuzzy Best–Worst Method (SFBWM). Supplier ranking was then performed using the Stochastic–Fuzzy TOPSIS (SFTOPSIS) technique. In the final phase, a multi-objective fuzzy–stochastic mathematical model was developed to design and optimize the supply chain network, while fuzzy–stochastic robust optimization was employed to address data uncertainty. The multi-objective problem was solved using a modified version of the Lexicographic–Chebyshev Multi-Choice Goal Programming Method (LCRMCGP).
Findings: A case study conducted in “Ebtakar Tajhiz Teb Yekta,” a company operating in the medical equipment industry, demonstrated that the proposed model effectively supports key strategic decisions, including the selection of primary and backup suppliers, the optimal location of collection and recycling centers, excess capacity allocation, and the choice of information-exchange technologies (traditional systems vs. blockchain-based platforms). The integration of IoT and blockchain technologies increased product return rates, reduced recycling costs, and enhanced transparency and sustainability across the network. Overall, the results confirm that the proposed framework can successfully balance economic, environmental, and social objectives while improving flexibility and resilience under uncertainty.
Originality/Value: The novelty of the present study lies in developing an integrated framework for designing a viable closed-loop supply chain under hybrid fuzzy–stochastic uncertainty. Unlike previous studies that mainly focused on isolated dimensions of supply chain management, this research simultaneously incorporates sustainability, resilience, agility, and digitalization within a multi-objective optimization model. Furthermore, the integration of SARIMA, SFBWM, SFTOPSIS, and LCRMCGP methods provides a more accurate and comprehensive decision-making process. Comparative results also demonstrate that the proposed model outperforms conventional approaches in reducing deviations, improving decision consistency, and enhancing overall network sustainability.

Keywords

Supply chain management
Viable supply chain
Closed-loop supply chain
Uncertainty
Subjects
[1]   Chen, D., Chu, F., Liu, M., & Huang, Y. (2025). A distribution-free-based approach for stochastic food closed-loop supply chain. International Journal of Production Research, 63(7), 2526–2555. https://doi.org/10.1080/00207543.2024.2406994
[2]   Zekhnini, K., Chaouni Benabdellah, A., & Cherrafi, A. (2024). A multi-agent based big data analytics system for viable supplier selection. Journal of Intelligent Manufacturing, 35(8), 3753-3773. https://doi.org/10.1007/s10845-023-02253-7
[3]   Asif, K., & Albherat, K. (2024). The impact of procurement strategies on supply chain sustainability in the pharmaceutical industry. South Asian Journal of Operations and Logistics, 3(2), 345-361. https://doi.org/10.57044/SAJOL.2024.3.2.2446
[4]   Malhotra, G. (2024). Impact of circular economy practices on supply chain capability, flexibility and sustainable supply chain performance. The International Journal of Logistics Management, 35(5), 1500-1521. https://doi.org/10.1108/IJLM-01-2023-0019
[5]   Dehshiri, S. J. H., & Amiri, M. (2024). Considering the circular economy for designing closed-loop supply chain under hybrid uncertainty: A robust scenario-based possibilistic-stochastic programming. Expert Systems with Applications, 238, 121745. https://doi.org/10.1016/j.eswa.2023.121745
[6]   Ruel, S., El Baz, J., Ivanov, D., & Das, A. (2024). Supply chain viability: Conceptualization, measurement, and nomological validation. Annals of Operations Research, 335(3), 1107–1136. https://doi.org/10.1007/s10479-021-03974-9
[7]   Ivanov, D. (2023). The Industry 5.0 framework: Viability-based integration of the resilience, sustainability, and human-centricity perspectives. International Journal of Production Research, 61(5), 1683–1695. https://doi.org/10.1080/00207543.2022.2118892
[8]   Sharma, M., & Joshi, S. (2023). Digital supplier selection reinforcing supply chain quality management systems to enhance firm’s performance. TQM Journal. https://doi.org/10.1108/TQM-07-2020-0160
[9]   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
[10] Goodarzian, F., Taleizadeh, A. A., Ghasemi, P., & Abraham, A. (2021). An integrated sustainable medical supply chain network during COVID-19. Engineering Applications of Artificial Intelligence, 100, 104188. https://doi.org/10.1016/j.engappai.2021.104188
[11] Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
[12] Alizadeh, M., Pishvaee, M. S., Jahani, H., Paydar, M. M., & Makui, A. (2023). Viable healthcare supply chain network design for a pandemic. Annals of Operations Research, 328(1), 35–73. https://doi.org/10.1007/s10479-022-04934-7%0A%0A
[13] Lotfi, R., Nazarpour, H., Gharehbaghi, A., Sarkhosh, S. M. H., & Khanbaba, A. (2022). Viable closed-loop supply chain network by considering robustness and risk as a circular economy. Environmental Science and Pollution Research, 29(46), 70285–70304. https://doi.org/10.1007/s11356-022-20713-0%0A%0A
[14] Zahari, M. K., Zakuan, N., Yusoff, M. E., Mat Saman, M. Z., Ali Khan, M. N. A., Muharam, F. M., & Yaacob, T. Z. (2023). Viable supply chain management toward company sustainability during covid-19 Pandemic in Malaysia. Sustainability (Switzerland), 15(5). https://doi.org/10.3390/su15053989
[15] 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. Springer, Cham. https://doi.org/10.1007/978-3-030-94335-6_14
[16] Zhu, C., Zhu, N., Zheng, S., Zou, L., & Wang, X. (2025). Analyzing critical success factors for green supplier selection: A combined DEMATEL-ISM approach and convolutional neural network based consensus model. Applied Soft Computing, 112760. https://doi.org/10.1016/j.asoc.2025.112760
[17] Nayeri, S., Sazvar, Z., & Babaee Tirkolaee, E. (2025). Viable supplier selection problem based on Industry 5.0 and circular economy aspects: A hybrid decision-making approach. International Journal of Systems Science: Operations & Logistics, 12(1), 2469117. https://doi.org/10.1080/23302674.2025.2469117
[18] Padovano, A., & Ivanov, D. (2025). Towards resilient and viable supply chains: A multidimensional model and empirical analysis. International Journal of Production Research, 63(17), 6252-6290. https://doi.org/10.1080/00207543.2025.2470350
[19] Lotfi, R., Safavi, S., Gharehbaghi, A., Ghaboulian Zare, S., Hazrati, R., & Weber, G. W. (2021). Viable supply chain network design by considering blockchain technology and cryptocurrency. Mathematical Problems in Engineering, 2021(1), 7347389. https://doi.org/10.1155/2021/7347389
[20] Kumar, D., Soni, G., Joshi, R., Jain, V., & Sohal, A. (2022). Modelling supply chain viability during COVID-19 disruption: A case of an Indian automobile manufacturing supply chain. Operations Management Research, 15(3), 1224-1240. https://doi.org/10.1007/s12063-022-00277-5
[21] Ajayi, M. O., & Laseinde, O. T. (2022, August). Promoting viable supply chain management (SCM) in the Nigeria agro-allied industry using internet of things. In Proceedings of Seventh International Congress on Information and Communication Technology: ICICT 2022, London, Volume 1 (pp. 389-399). Singapore: Springer Nature Singapore.. https://doi.org/10.1007/978-981-19-1607-6_34
[22] Chaouni Benabdellah, A., Zekhnini, K., Cherrafi, A., Garza-Reyes, J. A., Kumar, A., & El Baz, J. (2023). Blockchain technology for viable circular digital supplychains: an integrated approach for evaluating the implementation barriers. Benchmarking: An International Journal, 30(10), 4397-4424. https://doi.org/10.1108/BIJ-04-2022-0240
[23] Zhu, A., Han, Y., & Liu, H. (2024). Effects of adaptive cooperation among heterogeneous manufacturers on supply chain viability under fluctuating demand in post-COVID-19 era: An agent-based simulation. International Journal of Production Research, 62(4), 1162–1188. https://doi.org/10.1080/00207543.2023.2178370
[24] Lotfi, R., Hazrati, R., Aghakhani, S., Afshar, M., Amra, M., & Ali, S. S. (2024). A data-driven robust optimization in viable supply chain network design by considering Open Innovation and Blockchain Technology. Journal of Cleaner Production, 436, 140369. https://doi.org/10.1016/j.jclepro.2023.140369
[25] Ebhuoma, O., Gebreslasie, M., & Magubane, L. (2018). A seasonal autoregressive integrated moving average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa. South African Medical Journal, 108(7), 573-578. https://doi.org/10.7196/SAMJ.2018.v108i7.12885
[26] Avazpour, M., Zarei, J., & Alinezhad, E. (2025). Evaluation and prioritization of electricity generation technologies in Iran using a multi-criteria decision-making approach. System Engineering and Productivity, 5(3), 179-198. https://doi.org/10.22034/sep.2025.2063697.1333
[27] 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). Journal of Decisions And Operations Research, 9(4), 1045-1063. (In Persian). https://doi.org/10.22105/dmor.2025.498734.1904
[28] Asadi, Z., Aghajani, H., Khatir, M. V., & Tirkolaee, E. B. (2025). Viable-sustainable supplier selection and order allocation problem considering Industry 5.0 pillars under mixed uncertainty. International Journal of Production Research, 63(20), 7591-7616. https://doi.org/10.1080/00207543.2025.2502848
[29] Nayeri, S., Khoei, M. A., Rouhani Tazangi, M. R., GhanavatiNejad, M., Rahmani, M., & Tirkolaee, E. B. (2023). A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system. Engineering Applications of Artificial Intelligence, 124, 106511. https://doi.org/10.1016/j.engappai.2023.106511
[30] Kashanian Monfared, N., Safaie, N., & Hosseininezhad, S. J. (2025). A decision-making model for the problem of designing the layout of medical centers considering uncertainty. System Engineering and Productivity, 5(2), 97-118. https://doi.org/10.22034/sep.2025.2049327.1252
[31] Yazdani Hoshyar, A., & Keshvari, A. (2023). Investigating and formulating anthropogenic threats in refinery projects with a combination of AHP-TOPSIS method: A case study of Tehran oil refinery. System Engineering and Productivity, 2(4), 94–119. https://doi.org/10.22034/sep.2023.704334
[32] Sun, L., Yu, C., Li, J., Yuan, Q., & Zhao, S. (2025). A two-stage decision model for sustainable-resilient supplier selection and order allocation under uncertain environment. Kybernetes, 54(8), 4078-4113. https://doi.org/10.1108/K-11-2023-2347
[33] 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). https://doi.org/10.3390/asi5010008
[34] Hasani, A., Mokhtari, H., & Fattahi, M. (2021). A multi-objective optimization approach for green and resilient supply chain network design: A real-life case study. Journal of Cleaner Production, 278, 123199. https://doi.org/10.1016/j.jclepro.2020.123199
[35] Sazvar, Z., Zokaee, M., Tavakkoli Moghaddam, R., Salari, S. A., & Nayeri, S. (2021). Designing a sustainable closed-loop pharmaceutical supply chain in a competitive market considering demand uncertainty, manufacturer’s brand and waste management. Annals of Operations Research, 1–32. https://doi.org/10.1007/s10479-021-03961-0
[36] 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
[37] Mamashli, Z., Bozorgi Amiri, A., Dadashpour, I., Nayeri, S., & Heydari, J. (2021). A heuristic-based multi-choice goal programming for the stochastic sustainable-resilient routing-allocation problem in relief logistics. Neural Computing and Applications, 1–27. https://doi.org/10.1007/s00521-021-06074-8
[38] Khalili Nasr, A., Tavana, M., Alavi, B., & Mina, H. (2021). A novel fuzzy multi-objective circular supplier selection and order allocation model for sustainable closed-loop supply chains. Journal of Cleaner Production, 287, 124994. https://doi.org/10.1016/j.jclepro.2020.124994
Journal of Quality Engineering and Management
Volume 16, Issue 1
Spring 2026
Pages 39-62

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