[1] Fazli-Khalaf, M., Mirzazadeh, A., & Pishvaee, M. S. (2017). A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network. Human and ecological risk assessment: an international journal, 23(8), 2119-2149.
[2] Ghahremani Nahr, J., Pasandideh, S. H. R., & Niaki, S. T. A. (2020). A robust optimization approach for multi-objective, multi-product, multi-period, closed-loop green supply chain network designs under uncertainty and discount. Journal of industrial and production engineering, 37(1), 1-22.
[3] Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & industrial engineering, 116, 178-191.
[4] Mohtashami, Z., Aghsami, A., & Jolai, F. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of cleaner production, 242. https://doi.org/10.1016/j.jclepro.2019.118452
[5] Prakash, S., Kumar, S., Soni, G., Jain, V., & Rathore, A. P. S. (2020). Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach. Annals of operations research, 290(1), 837-864.
[6] Sadeghi, A., Mina, H., & Bahrami, N. (2020). A mixed integer linear programming model for designing a green closed-loop supply chain network considering location-routing problem. International journal of logistics systems and management, 36(2), 177-198.
[7] Safaei, A. S., Roozbeh, A., & Paydar, M. M. (2017). A robust optimization model for the design of a cardboard closed-loop supply chain. Journal of cleaner production, 166, 1154-1168.
[8] Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European journal of operational research, 240(3), 603-626.
[9] Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Van Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and operations management, 10(2), 156-173.
[10] Üster, H., Easwaran, G., Akçali, E., & Çetinkaya, S. (2007). Benders decomposition with alternative multiple cuts for a multi‐product closed‐loop supply chain network design model. Naval research logistics (NRL), 54(8), 890-907.
[11] Lee, D. H., & Dong, M. (2009). Dynamic network design for reverse logistics operations under uncertainty. Transportation research part E: logistics and transportation review, 45(1), 61-71.
[12] Lee, J. E., Gen, M., & Rhee, K. G. (2009). Network model and optimization of reverse logistics by hybrid genetic algorithm. Computers & industrial engineering, 56(3), 951-964.
[13] Kannan, G., Sasikumar, P., & Devika, K. (2010). A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied mathematical modelling, 34(3), 655-670.
[14] Khajavi, L. T., Seyed-Hosseini, S. M., & Makui, A. (2011). An integrated forward/reverse logistics network optimization model for multi-stage capacitated supply chain. iBusiness 3(2). DOI:10.4236/ib.2011.32030
[15] Das, K., & Chowdhury, A. H. (2012). Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning. International journal of production economics, 135(1), 209-221.
[16] Mahmoudi, H., Fazlollahtabar, H., & Mahdavi, I. (2013). Mathematical modeling for minimizing costs in a multilayer multi-product reverse supply chain. Industrial engineering & management, 2(2), 6.
[17] Dönmez, İ. (2013). Design of reverse logistics network for waste batteries with an application in Turkey. Chemical engineering transactions. Retrieved from https://d1wqtxts1xzle7.cloudfront.net/43286668/Design_of_Reverse_Logistics_Network_for_20160302-1112-2hyem.pdf?1456963233=&response-content-disposition=inline%3B+filename%3DDesign_of_reverse_logistics_network_for.pdf&Expires=1609333993&Signature=UnomU6E0H~gyPwvKaPdxBn261~518QLiB0DZdCPbT3PWXn6HDE7IQR9gFd7Elbdcn-8ij8Sg8rKfjqbxzyeyqm0RPfWaCxJ5XTGsZKLh6-cMk3UWuNLJjW~MRdOWAfV2kiz7gRTrEhR7PxbMPkF2hD-isDa6St3L4326fHpW3Xab9UPGc-rWAU1ROZfIodyIMVbL520s2P2ejeOU9pMPaLcoKeVV86bBmqrQSweT4Le88J43EvhQiENiqQtY1qXU6JI7hi12m0R6aqhJHm-pAi63nDOGXuPcFEb8UCz9qgxVyN63lkCc08~DSSD3yqesQ-kWaWqDiqSGyBIOQ4NlIg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
[18] Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied mathematical modelling, 37(1-2), 328-344.
[19] Özceylan, E., Paksoy, T., & Bektaş, T. (2014). Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. Transportation research part E: logistics and transportation review, 61, 142-164.
[20] Soleimani, H., & Govindan, K. (2014). Reverse logistics network design and planning utilizing conditional value at risk. European journal of operational research, 237(2), 487-497.
[21] Rezaee, A., Dehghanian, F., Fahimnia, B., & Beamon, B. (2017). Green supply chain network design with stochastic demand and carbon price. Annals of operations research, 250(2), 463-485.
[22] Alavi, S., Azad, N., Heydar, M., & Davoudpour, H. (2016). Integrated production, inventory, and location-allocation decisions in designing supply chain networks. International journal of information systems and supply chain management (IJISSCM), 9(4), 22-42.
[23] Nobil, A. H., & Taleizadeh, A. A. (2016). Analysing a fuzzy integrated inventory-production-distribution planning problem with maximum NPV of cash flows in a closed-loop supply chain. International Journal of Inventory Research, 3(1), 31-48.
[24] Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of cleaner production, 113, 662-673.
[25] Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation research part E: logistics and transportation review, 89, 182-214.
[26] Ciccullo, F., Pero, M., Caridi, M., Gosling, J., & Purvis, L. (2018). Integrating the environmental and social sustainability pillars into the lean and agile supply chain management paradigms: A literature review and future research directions. Journal of cleaner production, 172, 2336-2350.
[27] Das, K. (2018). Integrating lean systems in the design of a sustainable supply chain model. International Journal of Production Economics, 198, 177-190.
[28] Haddadsisakht, A., & Ryan, S. M. (2018). Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. International journal of production economics, 195, 118-131.
[29] Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2018). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy sets and systems, 341, 69-91.
[30] Darbari, J. D., Kannan, D., Agarwal, V., & Jha, P. C. (2019). Fuzzy criteria programming approach for optimising the TBL performance of closed loop supply chain network design problem. Annals of operations research, 273(1-2), 693-738.
[31] Ghahremani-Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert systems with applications, 116, 454-471.
[32] Samuel, C. N., Venkatadri, U., Diallo, C., & Khatab, A. (2020). Robust closed-loop supply chain design with presorting, return quality and carbon emission considerations. Journal of Cleaner Production, 247. https://doi.org/10.1016/j.jclepro.2019.119086
[33] Gholizadeh, H., Tajdin, A., & Javadian, N. (2020). A closed-loop supply chain robust optimization for disposable appliances. Neural computing and applications, 32(8), 3967-3985.
[34] Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied mathematical modelling, 35(2), 637-649.
[35] Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
[36] Altiparmak, F., Gen, M., Lin, L., & Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain network design. Computers & industrial engineering, 56(2), 521-537.
[37] Kim, K. W., Gen, M., & Yamazaki, G. (2003). Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling. Applied soft computing, 2(3), 174-188.
[38] Zegordi, S. H., Abadi, I. K., & Nia, M. B. (2010). A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain. Computers & industrial engineering, 58(3), 373-381.
[39] Hu, C., & Li, S. (2009). Two-phase interactive satisfying method of fuzzy multiple objective optimization with linguistic preference. International journal of information technology & decision making, 8(03), 427-443.
[40] Hamzadayi, A., & Yildiz, G. (2012). A genetic algorithm based approach for simultaneously balancing and sequencing of mixed-model U-lines with parallel workstations and zoning constraints. Computers & industrial engineering, 62(1), 206-215.
[41] Chitra, C., & Subbaraj, P. (2012). A nondominated sorting genetic algorithm solution for shortest path routing problem in computer networks. Expert systems with applications, 39(1), 1518-1525.
[42] Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. 2007 IEEE congress on evolutionary computation (pp. 4661-4667). IEEE.
[43] Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micro machine and human Science (pp. 39-43). IEEE.
[44] Nakrani, S., & Tovey, C. (2004). On honey bees and dynamic server allocation in internet hosting centers. Adaptive behavior, 12(3-4), 223-240.
[45] Lin, L., Gen, M., & Wang, X. (2009). Integrated multistage logistics network design by using hybrid evolutionary algorithm. Computers & industrial engineering, 56(3), 854-873.