مهندسی سیستم و بهره‌وری

مهندسی سیستم و بهره‌وری

ارزیابی و انتخاب تأمین‌کنندگان در زنجیره تأمین حلقه بسته بادوام تحت عدم قطعیت ترکیبی

نوع مقاله : پژوهشی

نویسندگان
1 دانشجوی دکتری، گروه مهندسی صنایع، دانشگاه علوم و فنون مازندران، بابل، ایران
2 نویسنده مسئول: استاد، گروه مهندسی صنایع، دانشگاه علوم و فنون مازندران، بابل، ایران
3 دانشیار، گروه مهندسی صنایع، دانشگاه علوم و فنون مازندران، بابل، ایران
چکیده
در سال‌های اخیر، پیشرفت فناوری، پیچیدگی محیط کسب‌وکار و بحران‌هایی مانند همه‌گیری کرونا، لزوم بازنگری در مدیریت زنجیره تأمین را افزایش داده است. رویکرد «زنجیره تأمین بادوام» با تمرکز بر تاب‌آوری، پایداری، چابکی و دیجیتالی‌سازی، راهکاری نوین برای بهبود کارایی بلندمدت سازمان‌هاست. در این پژوهش، با هدف ارزیابی و انتخاب تأمین‌کنندگان در زنجیره تأمین حلقه‌بسته تحت عدم‌قطعیت، ابتدا معیارهای کلیدی با مرور ادبیات و نظرات خبرگان شناسایی و با روش بهترین–بدترین فازی–تصادفی وزن‌دهی شدند. سپس تأمین‌کنندگان با استفاده از روش تاپسیس فازی–تصادفی ارزیابی و رتبه‌بندی گردیدند. نتایج نشان داد علاوه بر معیارهای سنتی مانند هزینه و کیفیت، شاخص‌هایی نظیر وجود تأمین‌کنندگان پشتیبان، مدیریت ضایعات و رعایت شرایط منصفانه کاری اهمیت بالایی دارند. مطالعه موردی در صنعت تجهیزات پزشکی، اثربخشی مدل را در شناسایی تأمین‌کنندگان برتر و ارتقای عملکرد زنجیره تأمین تأیید کرد و بر ضرورت تمرکز مدیران بر معیارهای پایداری و بلندمدت به‌جای رویکردهای صرفاً اقتصادی تأکید نمود. نوآوری اصلی این پژوهش در توسعه یک چارچوب تصمیم‌گیری دومرحله‌ای نوین است که به‌طور هم‌زمان ابعاد زنجیره تأمین بادوام، ساختار حلقه‌بسته و دو نوع عدم‌قطعیت فازی و تصادفی را در ارزیابی و انتخاب تأمین‌کنندگان ادغام می‌کند. استفاده هم‌زمان از روش بهترین–بدترین فازی–تصادفی برای وزن‌دهی معیارها و روش تاپسیس فازی–تصادفی برای رتبه‌بندی تأمین‌کنندگان، قابلیت مدل را در مدیریت شرایط پیچیده و پر ابهام افزایش داده و آن را از مطالعات پیشین متمایز می‌سازد.

تازه های تحقیق

  • توسعه مدل دومرحله‌ای نوین برای انتخاب تأمین‌کنندگان در زنجیره تأمین حلقه‌بسته بادوام با در نظر گرفتن عدم‌قطعیت.
  • شناسایی معیارهای کلیدی در ارزیابی تأمین‌کنندگان بادوام و وزن دهی با استفاده از روش بهترین بدترین فازی
  • استفاده از روش تاپسیس فازی-تصادفی برای رتبه‌بندی و انتخاب تأمین‌کنندگان بهینه در شرایط پیچیده

کلیدواژه‌ها
موضوعات

عنوان مقاله English

Evaluation and Selection of Suppliers in a Viable Closed-Loop Supply Chain under Mixed Uncertainty

نویسندگان English

Fariborz Kalashi 1
Iraj Mahdavi 2
Ali Tajdin 3
Javad Rezaeian 3
1 Ph.D. Student, Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
2 Corresponding author: Professor, Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
3 Associate Professor, Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
چکیده English

In recent years, advances in technology, increased business complexity, and crises such as COVID-19 have highlighted the need to rethink supply chain management. The resilient supply chain approach—emphasizing resilience, sustainability, agility, and digitalization—offers a modern pathway to long-term organizational efficiency. This study evaluates and selects suppliers for a closed-loop supply chain under uncertainty. Key criteria were identified through literature review and expert consultation, then weighted using the fuzzy–stochastic Best–Worst Method (BWM). Suppliers were subsequently assessed and ranked via the fuzzy–stochastic TOPSIS method. Findings reveal that beyond traditional factors such as cost and quality, aspects like backup supplier availability, waste management, and fair labor compliance are critical. A medical equipment industry case study validated the model’s effectiveness in identifying top suppliers and enhancing supply chain performance, underscoring the importance of sustainability-focused, long-term strategies over purely economic ones. The study’s novelty lies in a two-stage decision-making framework that integrates resilient supply chain principles, closed-loop structures, and dual uncertainties (fuzzy and stochastic), offering a robust tool for managing complex, ambiguous procurement environments.

کلیدواژه‌ها English

Supplier selection
Supply Chain Management
Viable supply chain
Stochastic Fuzzy Best-Worst Method
Stochastic Fuzzy TOPSIS

Copyright © Fariborz Kalashi, Iraj Mahdavi, Ali Tajdin, Javad Rezaeian

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

Ajayi, M. O., & Laseinde, O. T. (2023). Promoting viable supply chain management (SCM) in the Nigeria agro-allied industry using Internet of Things. In X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of Seventh International Congress on Information and Communication Technology (pp. 389–399). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-1607-6_34
Ala, A., Goli, A., Mirjalili, S., & Simic, V. (2024). A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design. Applied Soft Computing, 150, Article 111012. https://doi.org/10.1016/j.asoc.2023.111012
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
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(7), 1–26. https://doi.org/10.1080/00207543.2025.2502848
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, 29(36), 53598-53618. https://doi.org/10.1007/s11356-022-19105-1
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
Avazpoor, 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 (In Persian)
Azizinejad, H., Tavakoli, G., Ehsanifar, M., & Najafi, A. (2025). Explaining the factors affecting intellectual capital to facilitate productivity in knowledge-based businesses. System Engineering and Productivity, 5(2), 149–174. https://doi.org/10.22034/sep.2025.2055329.1299. (In Persian)
Babaei, A., Khedmati, M., Akbari Jokar, M. R., & Tirkolaee, E. B. (2023). Designing an integrated blockchain-enabled supply chain network under uncertainty. Scientific Reports, 13(1), Article 3928. https://doi.org/10.1038/s41598-023-30439-9
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.
https://doi.org/10.1007/s10479-024-06128-9
Bahrami, M. R., Hashemzadeh, G. R., Shahmansoury, A., & Fathi Hafshejani, K. (2025). Analyzing effective components in Industry 4.0 maturity for Iranian banking. System Engineering and Productivity, 5(1), 21–50. https://doi.org/10.22034/sep.2025.2047848.1246 (In Persian)
Bayatzadeh, S., & Talaie, H. (2024). Identifying and evaluating 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 (In Persian)
Belhadi, A., Kamble, S., Subramanian, N., Singh, R. K., & Venkatesh, M. (2024). Digital capabilities to manage agri-food supply chain uncertainties and build supply chain resilience during compounding geopolitical disruptions. International Journal of Operations & Production Management, 44(11), 1914–1950. https://doi.org/10.1108/IJOPM-11-2022-0737
Chaouni Benabdellah, A., Zekhnini, K., Cherrafi, A., Garza-Reyes, J. A., Kumar, A., & El Baz, J. (2023). Blockchain technology for viable circular digital supply chains: 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
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
De Lima, F. A., & Seuring, S. (2023). A Delphi study examining risk and uncertainty management in circular supply chains. International Journal of Production Economics, 258, Article 108810. https://doi.org/10.1016/j.ijpe.2023.108810
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(Part B), Article 121745. https://doi.org/10.1016/j.eswa.2023.121745
Dursun, M., & Ogunclu, O. (2021). Agile supplier evaluation using hierarchical TOPSIS method. WSEAS Transactions on Information Science and Applications, 18, 12–19. https://doi.org/10.37394/23209.2021.18.3
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, Article 100440. https://doi.org/10.1016/j.jii.2023.100440
Forouzeshnejad, A. A. (2023). Leagile and sustainable supplier selection problem in the Industry 4.0 era: A case study of the medical devices using hybrid multi-criteria decision making tool. Environmental Science and Pollution Research, 30(5), 13418–13437. https://doi.org/10.1007/s11356-022-22916-x
Gholamian, S. A. (2024). Evaluation and selection of sustainable suppliers by providing a decision support system based on a new data envelopment analysis model and cumulative star utility. System Engineering and Productivity, 4(1), 1–13. https://doi.org/10.22034/msb.2024.2025845.1198 (In Persian)
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, Article 102279. https://doi.org/10.1016/j.tre.2021.102279
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, Article 118317. https://doi.org/10.1016/j.jclepro.2019.118317
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, Article 132752. https://doi.org/10.1016/j.jclepro.2022.132752
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, 324(1), 395–435. https://doi.org/10.1007/s10479-021-03985-6
Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. International Journal of Production Research, 61(5), 1315–1333. https://doi.org/10.1080/00207543.2021.1890852
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
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 (In Persian)
Keyvani Shahri, F. S., Kaveh, D., Karimi, M., & Zendehdel, A. (2024). Identifying the dimensions and components of entrepreneurship with a social responsibility approach in the General Directorate of Education. System Engineering and Productivity, 4(2), 75–92. https://doi.org/10.22034/msb.2024.2032004.1216 (In Persian)
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
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), Article 8. https://doi.org/10.3390/asi5010008
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, Article 140369. https://doi.org/10.1016/j.jclepro.2023.140369
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
Lotfi, R., Safavi, S., Gharehbaghi, A., Zare, S. G., 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
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
Luo, L., Li, X., & Zhao, Y. (2025). A two-stage stochastic-robust model for supply chain network design problem under disruptions and endogenous demand uncertainty. Transportation Research Part E: Logistics and Transportation Review, 196, Article 104013. https://doi.org/10.1016/j.tre.2025.104013
Marques, C. M., Silva, A. C., & de Sousa, J. P. (2024). Inventory strategies for optimizing resiliency and sustainability in pharmaceutical supply chains—A simulation-optimization approach. In Computer Aided Chemical Engineering (Vol. 53, pp. 1825–1830). Elsevier. https://doi.org/10.1016/B978-0-443-28824-1.50305-7
Matli, K., Mahdi, A., Zibara, V., Costanian, C., & Ghanem, G. (2022). Transcatheter tricuspid valve intervention techniques and procedural steps for the treatment of tricuspid regurgitation: a review of the literature. Open Heart9(1). https://doi.org/10.1136/openhrt-2022-002030
Mozafari, M., & Savari, J. (2025). Designing a green closed-loop supply chain network for pharmaceutical products using cuckoo search algorithm. System Engineering and Productivity, 5(1), 135–153. https://doi.org/10.22034/sep.2025.2050452.1248 (In Persian)
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), Article 2469117. https://doi.org/10.1080/23302674.2025.2469117
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(Part B), Article 119267. https://doi.org/10.1016/j.eswa.2022.119267
Padovano, A., & Ivanov, D. (2025). Towards resilient and viable supply chains: a multidimensional model and empirical analysis. International Journal of Production Research, 1-39. https://doi.org/10.1080/00207543.2025.2470350
Sherafati, M., Bashiri, M., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2019). Supply chain network design considering sustainable development paradigm: A case study in cable industry. Journal of Cleaner Production, 234, 366–380. https://doi.org/10.1016/j.jclepro.2019.06.095
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. Kybernetes54(8), 4078-4113. https://doi.org/10.1108/K-11-2023-2347
Tao, Y., Zhu, S., Smith, J., Lakhani, N., & You, F. (2023). Environmental sustainability of the globalized pharmaceutical supply chains: The case of tenofovir disoproxil fumarate. ACS Sustainable Chemistry & Engineering, 11(17), 6510–6522. https://doi.org/10.1021/acssuschemeng.2c06518
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 (In Persian)
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, 15(5), Article 3989. https://doi.org/10.3390/su15053989
Zekhnini, K., Chaouni Benabdellah, A., & Cherrafi, A. (2024). A multi-agent based big data analytics system for viable supplier selection. Journal of Intelligent Manufacturing35(8), 3753-3773. https://doi.org/10.1007/s10845-023-02253-7
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
دوره 5، شماره 4 - شماره پیاپی 17
شماره پیاپی 17، فصلنامه زمستان
زمستان 1404
صفحه 191-215

  • تاریخ دریافت 24 تیر 1404
  • تاریخ بازنگری 25 مرداد 1404
  • تاریخ پذیرش 29 مرداد 1404
  • تاریخ اولین انتشار 29 مرداد 1404
  • تاریخ انتشار 01 دی 1404