محیط رقابتی عصر حاضر، توجه سازمان‌ها را به رعایت الزامات کیفیت و مسئولیت‌پذیری اجتماعی معطوف داشته است. زیرا سازمان‌هایی که خود را پای‌بند به چارچوب مدیریت کیفیت می‌دانند به سطح بالاتری از رضایتمندی مشتریان دست می‌یابند. این تحقیق برای ادغام پایداری و قابلیت اطمینان، مسئله طراحی شبکه زنجیره تأمین اقتصادی، مسئولیت‌پذیر و قابل اطمینان را به صورت جامع و کارآمد مورد مدل‌سازی قرار داده است. از این رو، یک مدل برنامه‌ریزی عدد صحیح مختلط غیرخطی برای مسئله طراحی شبکه زنجیره تأمین به صورت سه هدفه، چندمحصولی، چندسطحی، چندمنبعی، چندظرفیتی و چندمرحله‌ای در نظر گرفته شده است. جواب‌های بهینه پارتو مدل پیشنهادی، با استفاده از روش محدودیت اپسیلون تکامل یافته (AEC) به دست آمده است. همچنین از مثال عددی با داده‌های تصادفی برای سنجش صحت و عملکرد کلی مدل پیشنهادی استفاده شده است. نتایج نشان داد که با افزایش پارامتر تقاضا مقدار سود مسئله افزایش می‌یابد. این در حالی است که قابلیت اطمینان و مسئولیت‌پذیری اجتماعی کاهش پیدا می‌کند. علاوه بر این، با افزایش انتشار گازهای گلخانه‌ای مقدار تابع هدف سود و مسئولیت‌پذیری اجتماعی کاهش پیدا می‌کنند در حالی که مقدار تابع هدف قابلیت اطمینان تقریباً ثابت می‌ماند. با این حال، دستیابی به هم‌افزایی میان پایداری و قابلیت اطمینان در طراحی شبکه زنجیره تأمین، نیازمند تدوین دستورالعمل‌های دقیق‌تر و بلندمدت‌تری است.
اسکندری‌ثانی، محمد و سفالگر، سحر (1401)، ادغام اقتصاد سبز و چرخشی، رویکرد نوین درآمد پایدار در شهر بیرجند. نشریه مدیریت سبز و توسعه، 1 (2)، ص 159-172. https://doi.org/10.22077/jgmd.2023.6171.1023
امیریان، سجاد، امیری، مقصود و تقوی فرد، محمدتقی (1402)، ادغام پایداری و قابلیت اطمینان در زنجیره تأمین: مرور سیستماتیک ادبیات. نشریه علمی مدیریت زنجیره تأمین، 25 (78)، ص 1-28. https://dorl.net/dor/20.1001.1.20089198.1402.25.79.8.2
پاشایی، پارسا، شاطری، مفید و اشرفی، علی (1401)، تحلیل پروژه‌های محیط زیستی اجرا شده در شرکت پالایشگاه گاز فجر جم. نشریه مدیریت سبز و توسعه، 1 (2)، ص 13-26. https://doi.org/10.22077/jgmd.2023.6102.1019
Ahi, P., & Searcy, C. (2015). An analysis of metrics used to measure performance in green and sustainable supply chains. Journal of cleaner production, 86, 360-377. https://doi.org/10.1016/j.jclepro.2014.08.005
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176. https://doi.org/10.1016/j.apm.2012.09.039
Amirian, S., Amiri, M., & Taghavifard, M. T. (2022a). The Emergence of a Sustainable and Reliable Supply Chain Paradigm in Supply Chain Network Design. Complexity, 2022. https://doi.org/10.1155/2022/9415465
Amirian, S., Amiri, M., & Taghavifard, M. T. (2022b). Sustainable and reliable closed-loop supply chain network design: Normalized Normal Constraint (NNC) method application. Journal of Industrial and Systems Engineering, 14(3), 33-68. https://dorl.net/dor/20.1001.1.17358272.2022.14.3.2.1
Barros, R. D. C., Sampaio, M., & Correa, J. S. (2019). Impact of the inclusion of variable CO 2 cost in the distribution network design. Production, 29. https://doi.org/10.1590/0103-6513.20190065
Basu, D., & Lee, M. (2022). A combined sustainability-reliability approach in geotechnical engineering. In Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering (pp. 379-413). Elsevier. http://dx.doi.org/10.1016/B978-0-323-85698-0.00029-0
Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. In Das summa summarum des management (pp. 265-275). Gabler. https://doi.org/10.1007/978-3-8349-9320-5_22
Esmizadeh, Y., & Mellat Parast, M. (2021). Logistics and supply chain network designs: incorporating competitive priorities and disruption risk management perspectives. International Journal of Logistics Research and Applications, 24(2), 174-197. https://doi.org/10.1080/13675567.2020.1744546
Fazli-Khalaf, M., Naderi, B., Mohammadi, M., & Pishvaee, M. S. (2020). Design of a sustainable and reliable hydrogen supply chain network under mixed uncertainties: A case study. International Journal of Hydrogen Energy, 45(59), pp. 34503-34531. http://dx.doi.org/10.1016/j.ijhydene.2020.05.276
Galaitsi, S. E., Keisler, J. M., Trump, B. D., & Linkov, I. (2021). The need to reconcile concepts that characterize systems facing threats. Risk Analysis, 41(1), 3-15. https://doi.org/10.1111/risa.13577
Gao, Y., Li, J., & Song, Y. (2009, August). Performance evaluation of green supply chain management based on membership conversion algorithm. In 2009 ISECS international colloquium on computing, communication, control, and management (Vol. 3, pp. 237-240). https://doi.org/10.1109/CCCM.2009.5267895
Ghayebloo, S., Tarokh, M. J., Venkatadri, U., & Diallo, C. (2015). Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products: the impact of parts reliability and product greenness on the recovery network. Journal of Manufacturing Systems, 36, 76-86. http://dx.doi.org/10.1016/j.jmsy.2015.02.011
Giunipero, L. C., & Eltantawy, R. A. (2004). Securing the upstream supply chain: a risk management approach. International Journal of Physical Distribution & Logistics Management. 34(9), 698-713 http://dx.doi.org/10.1108/09600030410567478
Gottberg, A., Morris, J., Pollard, S., Mark-Herbert, C., & Cook, M. (2006). Producer responsibility, waste minimisation and the WEEE Directive: Case studies in eco-design from the European lighting sector. Science of the total environment, 359(1-3), 38-56. https://doi.org/10.1016/j.scitotenv.2005.07.001
Goudarzi, Z., Seifbarghy, M., & Pishva, D. (2022). Bi-objective modeling of a closed-loop multistage supply chain considering the joint assembly center and reliability of the whole chain. Journal of Industrial and Production Engineering, 39(3), 230-252. http://dx.doi.org/10.1080/21681015.2021.1974109
Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263(1), pp.108-141. https://doi.org/10.1016/j.ejor.2017.04.009
Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138, 101967. https://doi.org/10.1016/j.tre.2020.101967
Grishko, A., Adnreev, P., Goryachev, N., Trusov, V., & Danilova, E. (2018, May). Reliability control of complex systems at different stages of their life cycle. In 2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) (pp. 220-223). IEEE. http://dx.doi.org/10.1109/USBEREIT.2018.8384589
Hagspiel, V., Huisman, K. J., & Kort, P. M. (2016). Volume flexibility and capacity investment under demand uncertainty. International Journal of Production Economics, 178, 95-108. https://doi.org/10.1016/j.ijpe.2016.05.007
Hwang, C. L., & Masud, A. S. M. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164). Springer Science & Business Media. https://link.springer.com/book/10.1007/978-3-642-45511-7
Kamalahmadi, M., & Mellat-Parast, M. (2016). Developing a resilient supply chain through supplier flexibility and reliability assessment. International Journal of Production Research, 54(1), 302-321. http://dx.doi.org/10.1080/00207543.2015.1088971
Khalifehzadeh, S., Seifbarghy, M., & Naderi, B. (2015). A four-echelon supply chain network design with shortage: Mathematical modeling and solution methods. Journal of Manufacturing Systems, 35, 164-175. https://doi.org/10.1016/j.jmsy.2014.12.002
Khan, S. A. R., Zkik, K., Belhadi, A., & Kamble, S. S. (2021). Evaluating barriers and solutions for social sustainability adoption in multi-tier supply chains. International Journal of Production Research, 59(11), 3378-3397. https://doi.org/10.1080/00207543.2021.1876271
Li, Q., Loy-Benitez, J., Nam, K., Hwangbo, S., Rashidi, J., & Yoo, C. (2019). Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks. Energy, Vol. 178, pp. 277-292. https://doi.org/10.1016/j.energy.2019.04.114
Marchi, B., Zanoni, S., Zavanella, L. E., & Jaber, M. Y. (2019). Supply chain models with greenhouse gases emissions, energy usage, imperfect process under different coordination decisions. International Journal of Production Economics, Vol. 211, pp. 145-153. https://doi.org/10.1016/j.ijpe.2019.01.017
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), pp. 455-465. https://doi.org/10.1016/j.amc.2009.03.037
Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669. https://doi.org/10.1016/j.amc.2013.03.002
Nosrati, M., & Khamseh, A. (2020). Reliability optimization in a four-echelon green closed-loop supply chain network considering stochastic demand and carbon price. Uncertain Supply Chain Management, 8(3), pp.457-472. http://dx.doi.org/10.5267/j.uscm.2020.5.002
Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, Vol. 183, pp. 421-432. https://doi.org/10.1016/j.ijpe.2016.08.028
Ohmori, S., & Yoshimoto, K. (2013). A framework of managing supply chain disruption risks using network reliability. Industrial engineering and management systems, 12(2), 103-111. http://dx.doi.org/10.7232/iems.2013.12.2.103
Okwu, M. O., & Tartibu, L. K. (2020). Sustainable supplier selection in the retail industry: A TOPSIS-and ANFIS-based evaluating methodology. International journal of engineering business management, 12, 1847979019899542. http://dx.doi.org/10.1177/1847979019899542
Pagell, M., & Wu, Z. (2009). Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. Journal of supply chain management, 45(2), 37-56. https://doi.org/10.1111/j.1745-493X.2009.03162.x
Pereira, J. L. J., Oliver, G. A., Francisco, M. B., Cunha, S. S., & Gomes, G. F. (2021). A review of multi-objective optimization: methods and algorithms in mechanical engineering problems. Archives of Computational Methods in Engineering, 1-24. https://doi.org/10.1007/s11831-021-09663-x
Psarommatis, F., & May, G. (2022). A standardized approach for measuring the performance and flexibility of digital twins. International Journal of Production Research, 1-16. http://dx.doi.org/10.1080/00207543.2022.2139005
Rahmani, D., & Mahoodian, V. (2017). Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness. Journal of Cleaner Production, 149, 607-620. https://doi.org/10.1016/j.jclepro.2017.02.068
Samuel, C. N., Diallo, C., Venkatadri, U., & Ghayebloo, S. (2021). Multicomponent multiproduct closed-loop supply chain design with transshipment and economies of scale considerations. Computers & Industrial Engineering, 153, 107073. http://dx.doi.org/10.1016/j.cie.2020.107073
Singh, S., Kumar, R., Panchal, R., & Tiwari, M. K. (2021). Impact of COVID-19 on logistics systems and disruptions in food supply chain. International journal of production research, 59(7), 1993-2008. https://doi.org/10.1080/00207543.2020.1792000
Tirkolaee, E. B., Goli, A., Faridnia, A., Soltani, M., & Weber, G. W. (2020). Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms. Journal of Cleaner Production, 276, 122927. https://doi.org/10.1016/j.jclepro.2020.122927
Vishnu, C. R., Sridharan, R., Gunasekaran, A., & Kumar, P. R. (2019). Strategic capabilities for managing risks in supply chains: current state and research futurities. Journal of Advances in Management Research, 17(2), 173-211. https://doi.org/10.1108/JAMR-04-2019-0061
Wang, R., Ke, C., & Cui, S. (2022). Product price, quality, and service decisions under consumer choice models. Manufacturing & Service Operations Management, 24(1), 430-447. http://dx.doi.org/10.1287/msom.2020.0947
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