System Engineering and Productivity

System Engineering and Productivity

Designing a Green Closed-Loop Supply Chain Network for Pharmaceutical Products Using Cuckoo Search Algorithm

Document Type : Research Paper

Authors
Marzieh Mozafari 1
Jafar Savari 2
1 Corresponding author: Assistant Professor, Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 M.Sc., Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
10.22034/sep.2025.2050452.1248
Abstract
Pharmaceutical companies face complex challenges in designing and managing their supply chains due to regulatory requirements at the national, international, and internal levels, along with government-imposed constraints such as the sourcing of raw materials, distribution, exchange rates, and production and storage conditions. Designing green and closed-loop supply chain networks can play a crucial role in reducing costs, increasing efficiency, and minimizing the environmental impact of the industry. In this paper, a model for designing green and closed-loop supply chains for pharmaceutical products is proposed, focusing on the optimal location of production, distribution, and recycling centers. The proposed model takes into account both internal factors, such as the selection of raw materials and green technologies, and external factors like optimal site selection and transportation system optimization. The goal of this model is to minimize fixed and operational costs, reduce environmental pollutant emissions, and enhance supply chain sustainability. To solve the problem, mathematical modeling was applied using GAMS software, and the Cuckoo Search Algorithm (CSA) was implemented in MATLAB software. This approach provides optimal and efficient solutions for complex problems, yielding reliable results. The findings of this research indicate that the proposed approach can play a significant role in improving performance and greening pharmaceutical supply chains.

Highlights

  • Development of a multi-objective mathematical model for the green closed-loop supply chain design.
  • Incorporation of product returns due to perishability and recall of side-effect medications in the pharmaceutical supply chain
  • Development of a Cuckoo Search Algorithm to solve the multi-objective model in large-scale instances

Keywords
Closed-Loop Supply Chain Design
Pharmaceutical Products
Product Returns
Green Supply Chain
Cuckoo Search Algorithm
Subjects

Copyright © Marzieh Mozafari, Jafar Savari

 

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.

 

Alnahas, F., Yeboah, P., Fliedel, L., Abdin, A. Y., & Alhareth, K. (2020). Expired medication: Societal, regulatory and ethical aspects of a wasted opportunity. International Journal of Environmental Research and Public Health, 17(3), Article 787. https://doi.org/10.3390/ijerph17030787
Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Tavakkoli-Moghaddam, R. (2018). The social engineering optimizer (SEO). Engineering Applications of Artificial Intelligence, 72, 267–293. https://doi.org/10.1016/j.engappai.2018.04.009
Firoozi, Z., Ismail, N., Ariafar, S., Tang, S. H., Ariffin, M. K. A. M., & Memariani, A. (2013). Distribution network design for fixed lifetime perishable products: A model and solution approach. Journal of Applied Mathematics, 2013, Article 891409. https://doi.org/10.1155/2013/891409
Garai, A., & Sarkar, B. (2022). Economically independent reverse logistics of customer-centric closed-loop supply chain for herbal medicines and biofuel. Journal of Cleaner Production, 334, Article 129977. https://doi.org/10.1016/j.jclepro.2021.129977
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. https://doi.org/10.1016/j.eswa.2018.09.027
Goodarzian, F., Hosseini-Nasab, H., Muñuzuri, J., & Fakhrzad, M. B. (2020). A multi-objective pharmaceutical supply chain network based on a robust fuzzy model: A comparison of meta-heuristics. Applied Soft Computing, 92, Article 106331. https://doi.org/10.1016/j.asoc.2020.106331
Goodarzian, F., Navaei, A., Ehsani, B., Ghasemi, P., & Muñuzuri, J. (2023). Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: Artificial intelligence-based solutions. Annals of Operations Research, 328(1), 531–575. https://doi.org/10.1007/s10479-022-04713-4
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, Article 104188. https://doi.org/10.1016/j.engappai.2021.104188
Haial, A., Berrado, A., & Benabbou, L. (2020). Redesigning a transportation network: The case of a pharmaceutical supply chain. International Journal of Logistics Systems and Management, 35(1), 90–118. https://doi.org/10.1504/IJLSM.2020.103866
Hajiaghaei-Keshteli, M., & Aminnayeri, M. (2014). Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm. Applied Soft Computing, 25, 184–203. https://doi.org/10.1016/j.asoc.2014.09.034
Hsu, C.-I., Hung, S.-F., & Li, H.-C. (2007). Vehicle routing problem with time-windows for perishable food delivery. Journal of Food Engineering, 80(2), 465–475. https://doi.org/10.1016/j.jfoodeng.2006.05.029
Jiang, S., & Ma, Z. (2009). A hybrid genetic algorithm for the stochastic dynamic location-routing-inventory problem in closed-loop logistics system for reusing end-of-use products. In International Conference on Transportation Engineering 2009 (pp. 3433–3438). American Society of Civil Engineers. https://doi.org/10.1061/41039(345)668
Jindal, A., Sangwan, K. S., & Saxena, S. (2015). Network design and optimization for multi-product, multi-time, multi-echelon closed-loop supply chain under uncertainty. Procedia CIRP, 29, 656–661. https://doi.org/10.1016/j.procir.2015.01.024
Kalantari, M., & Pishvaee, M. S. (2016). A robust possibilistic programming approach to drug supply chain master planning. Journal of Industrial Engineering Research in Production Systems, 4(7), 49–67. (In Persian)
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (Vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
Kirkpatrick, S., Gelatt, C. D., Jr., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. https://doi.org/10.1126/science.220.4598.671
Levis, A. A., & Papageorgiou, L. G. (2004). A hierarchical solution approach for multi-site capacity planning under uncertainty in the pharmaceutical industry. Computers & Chemical Engineering, 28(5), 707–725. https://doi.org/10.1016/j.compchemeng.2004.02.012
Li, J., Yang, Y. H., Lei, H., & Wang, G. G. (2021). Solving logistics distribution center location with improved cuckoo search algorithm. International Journal of Computational Intelligence Systems, 14(1), 676–692. https://doi.org/10.2991/ijcis.d.201216.002
Liu, S. W., Dong, H., & Zhao, W. L. (2013). Optimization model based on the fractal theory in supply chain management. Advanced Materials Research, 694–697, 3554–3557. https://doi.org/10.4028/www.scientific.net/AMR.694-697.3554
Masoumi, A. H., Yu, M., & Nagurney, A. (2012). A supply chain generalized network oligopoly model for pharmaceuticals under brand differentiation and perishability. Transportation Research Part E: Logistics and Transportation Review, 48(4), 762–780. https://doi.org/10.1016/j.tre.2012.01.001
Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106–119. https://doi.org/10.1016/j.eswa.2015.10.039
Mirzapour Al-e-hashem, S. M. J., Baboli, A., & Sazvar, Z. (2013). A stochastic aggregate production planning model in a green supply chain: Considering flexible lead times, nonlinear purchase and shortage cost functions. European Journal of Operational Research, 230(1), 26–41. https://doi.org/10.1016/j.ejor.2013.03.033
Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & Operations Research, 24(11), 1097–1100. https://doi.org/10.1016/S0305-0548(97)00031-2
Mousazadeh, M., Torabi, S. A., & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers & Chemical Engineering, 82, 115–128. https://doi.org/10.1016/j.compchemeng.2015.06.008
Oh, H.-C., & Karimi, I. A. (2004). Regulatory factors and capacity-expansion planning in global chemical supply chains. Industrial & Engineering Chemistry Research, 43(13), 3364–3380. https://doi.org/10.1021/ie034339g
Osaba, E., Yang, X.-S., Fister, I., Jr., Del Ser, J., Lopez-Garcia, P., & Vazquez-Pardavila, A. J. (2019). A discrete and improved bat algorithm for solving a medical goods distribution problem with pharmacological waste collection. Swarm and Evolutionary Computation, 44, 273–286. https://doi.org/10.1016/j.swevo.2018.04.001
Papageorgiou, L. G., Rotstein, G. E., & Shah, N. (2001). Strategic supply chain optimization for the pharmaceutical industries. Industrial & Engineering Chemistry Research, 40(1), 275–286. https://doi.org/10.1021/ie990870t
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. https://doi.org/10.1016/j.fss.2010.04.010
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. https://doi.org/10.1016/j.apm.2010.07.013
Ranjbar, Z., & Mirzazadeh, A. (2019). The forward and reverse pharmaceutical supply chain network design considering tainted product delivery. International Journal of Logistics Systems and Management, 33(2), 205–220. https://doi.org/10.1504/IJLSM.2019.100110
Razmjooei, V., Mahdavi, I., & Paydar, M. M. (2022). A fuzzy bi-objective optimization model to design a reverse supply chain network: A cuckoo optimization algorithm. International Journal of Supply and Operations Management, 9(3), 360–378. https://doi.org/10.22034/ijsom.2021.108984.2107
Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657–672. https://doi.org/10.1016/j.cie.2018.10.001
Safdar, K., Abdul Rani, K. N., Rahim, H. A., Rosli, S. J., & Jamlos, M. A. (2023). A review on research trends in using cuckoo search algorithm: Applications and open research challenges. Przeglad Elektrotechniczny, 99(5), 25–30. https://doi.org/10.15199/48.2023.05.04
Sazvar, Z., Zokaee, M., Tavakkoli-Moghaddam, R., Salari, S. A. S., & Nayeri, S. (2022). 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, 315(2), 2057–2088. https://doi.org/10.1007/s10479-021-03961-0
Shekoohi Tolgari, F., & Zarrinpoor, N. (2024). A robust reverse pharmaceutical supply chain design considering perishability and sustainable development objectives. Annals of Operations Research, 340(2–3), 981–1033. https://doi.org/10.1007/s10479-024-05871-3
Sheu, J. B. (2008). Green supply chain management, reverse logistics and nuclear power generation. Transportation Research Part E: Logistics and Transportation Review, 44(1), 19–46. https://doi.org/10.1016/j.tre.2006.06.001
Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & Industrial Engineering, 109, 191–203. https://doi.org/10.1016/j.cie.2017.04.038
Susarla, N., & Karimi, I. A. (2012). Integrated supply chain planning for multinational pharmaceutical enterprises. Computers & Chemical Engineering, 42, 168–177. https://doi.org/10.1016/j.compchemeng.2012.03.002
Tirkolaee, E. B., Goli, A., Ghasemi, P., & Goodarzian, F. (2022). Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms. Journal of Cleaner Production, 333, Article 130056. https://doi.org/10.1016/j.jclepro.2021.130056
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193–214. https://doi.org/10.1016/j.fss.2007.08.010
Torabi, S. A., Namdar, J., Hatefi, S. M., & Jolai, F. (2016). An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. International Journal of Production Research, 54(5), 1358–1387. https://doi.org/10.1080/00207543.2015.1070215
Tucker, E. (2020). Modeling pharmaceutical supply chains to mitigate drug shortages (Doctoral dissertation, University of Pittsburgh).
Ullah, M., Asghar, I., Zahid, M., Omair, M., AlArjani, A., & Sarkar, B. (2021). Ramification of remanufacturing in a sustainable three-echelon closed-loop supply chain management for returnable products. Journal of Cleaner Production, 290, Article 125609. https://doi.org/10.1016/j.jclepro.2020.125609
Vila-Parrish, A. R., Ivy, J. S., King, R. E., & Abel, S. R. (2012). Patient-based pharmaceutical inventory management: A two-stage inventory and production model for perishable products with Markovian demand. Health Systems, 1(1), 69–83. https://doi.org/10.1057/hs.2012.2
Weraikat, D., Zanjani, M. K., & Lehoux, N. (2016). Coordinating a green reverse supply chain in pharmaceutical sector by negotiation. Computers & Industrial Engineering, 93, 67–77. https://doi.org/10.1016/j.cie.2015.12.026
Yang, G., Wang, Z., & Li, X. (2009). The optimization of the closed-loop supply chain network. Transportation Research Part E: Logistics and Transportation Review, 45(1), 16–28. https://doi.org/10.1016/j.tre.2008.02.007
Yang, X.-S. (2009). Firefly algorithms for multimodal optimization. In O. Watanabe & T. Zeugmann (Eds.), Stochastic algorithms: Foundations and applications (pp. 169–178). Springer. https://doi.org/10.1007/978-3-642-04944-6_14
Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. In J. R. González, D. A. Pelta, C. Cruz, G. Terrazas, & N. Krasnogor (Eds.), Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65–74). Springer. https://doi.org/10.1007/978-3-642-12538-6_6
Yang, X.-S., & Deb, S. (2009). Cuckoo search via Lévy flights. In A. Abraham, A. Carvalho, F. Herrera, & V. Pai (Eds.), 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 210–214). IEEE. https://doi.org/10.1109/NABIC.2009.5393690
Yavari, M., & Geraeli, M. (2019). Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of Cleaner Production, 226, 282–305. https://doi.org/10.1016/j.jclepro.2019.03.279
Zahiri, B., Jula, P., & Tavakkoli-Moghaddam, R. (2018). Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences, 423, 257–283. https://doi.org/10.1016/j.ins.2017.09.046
System Engineering and Productivity
Volume 5, Issue 1 - Serial Number 14
Serial No. 14, Spring Quarterly
Spring 2025
Pages 135-153

  • Receive Date 11 January 2025
  • Revise Date 10 February 2025
  • Accept Date 05 March 2025
  • First Publish Date 05 March 2025
  • Publish Date 22 May 2025