Journal of Quality Engineering and Management

Designing an integrated green supply chain model with an emphasis on improving environmental quality and increasing customer satisfaction

Document Type : Original Article

Author

Abdollah Arasteh

Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran.

https://doi.org/10.48313/jqem.2025.514959.1511
Abstract
Purpose: The purpose of this paper is to address one of the most critical challenges faced by organizations today: controlling carbon dioxide emissions. This study aims to provide a model for designing a green supply chain network that minimizes total network costs while incorporating environmental considerations. The research seeks to achieve a balanced optimization of costs, carbon emissions, and service levels in supply chain management.
Methodology: This study proposes a novel integrated optimization model that considers economic, environmental, and customer satisfaction aspects within the supply chain network. The mathematical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. An exact method is employed to solve the model, which is coded and implemented using GAMS optimization software. The efficiency and effectiveness of the model are validated through numerical examples and data analysis.
Findings: The results demonstrate the model's ability to optimize both economic and environmental dimensions while maintaining high service levels and customer satisfaction. The numerical examples, solved for problems of varying dimensions, confirm the practicality and effectiveness of the proposed approach. The findings highlight the trade-offs between cost minimization, carbon emission reduction, and service quality in supply chain networks.
Originality/Value: This research contributes to the field by presenting a new integrated optimization model that simultaneously addresses cost efficiency, environmental sustainability, and customer satisfaction in green supply chain design. The use of a mixed-integer nonlinear programming approach and its implementation in GAMS provides a robust framework for solving complex supply chain problems. The study offers valuable insights for organizations aiming to achieve sustainability goals while maintaining economic viability and customer-centric operations.

Keywords

Carbon emission
Green supply chain
Optimization
Service level
Supply chain management
Subjects
[1]    Anderson, T. R., Hawkins, E., & Jones, P. D. (2016). CO2, the greenhouse effect and global warming: From the pioneering work of Arrhenius and Callendar to today’s earth system models. Endeavour, 40(3), 178–187. https://doi.org/10.1016/j.endeavour.2016.07.002
[2]    Jauhari, W. A., Cahaya Sakti, C. T., Hisjam, M., & Hishamuddin, H. (2025). A sustainable circular economic supply chain model with green production, delays in payment, and Carbon tax regulation. Journal of cleaner production, 495, 145008. https://doi.org/10.1016/j.jclepro.2025.145008
[3]   Lamb, W. F., Wiedmann, T., Pongratz, J., Andrew, R., Crippa, M., Olivier, J. G. J., Wiedenhofer, D., Mattioli, G., Al Khourdajie1, A., House, J., Pachauri, S., A Figueroa, M., Saheb, M., Slade, R., Hubacek, K., Sun, L., Kahn Ribeiro, S., Khennas, S., De La Rue Du Can, S., Chapungu, L., J Davis, S., Bashmakov, I., Dai, H., Dhakal, SH., Tan, X., Geng, Y., Gu, B., & Minx, J. (2021). A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environmental research letters, 16(7), 73005. https://doi.org/10.1088/1748-9326/ABEE4E
[4]    Amiri, M., & Bayatzadeh, S. (2025). Identifying and ranking digital solutions for managing supply chain disruptions (Case study: Steel industry). Modern research in performance evaluation, 3(4), 240–253. https://doi.org/10.22105/mrpe.2025.499771.1137
[5]     Leung, D. Y. C., Caramanna, G., & Maroto-Valer, M. M. (2014). An overview of current status of Carbon Dioxide capture and storage technologies. Renewable and sustainable energy reviews, 39, 426–443. https://doi.org/10.1016/J.RSER.2014.07.093
[6]  Min, H., & Kim, I. (2012). Green supply chain research: Past, present, and future. Logistics research, 4, 39–47. https://doi.org/10.1007/s12159-012-0071-3
[7]    Saadati, H., & Hakimi, A. (2024). Optimizing the ticket response process in customer support systems using data-driven and machine learning methods: A Case study of IFDA. Optimality, 1(2), 188–204. https://doi.org/0.22105/opt.v1i2.57
[8]    Tseng, M. L., Islam, M. S., Karia, N., Fauzi, F. A., & Afrin, S. (2019). A literature review on green supply chain management: Trends and future challenges. Undefined, 141, 145–162. https://doi.org/10.1016/J.RESCONREC.2018.10.009
[9]    Aldrighetti, R., Battini, D., Ivanov, D., & Zennaro, I. (2021). Costs of resilience and disruptions in supply chain network design models: A review and future research directions. International journal of production economics, 235, 108103. https://doi.org/10.1016/J.IJPE.2021.108103
[10]  Ekram Nosratian, N., & Taghavi Fard, M. T. (2023). A proposed model for the assessment of supply chain management using DEA and knowledge management. Computational algorithms and numerical dimensions, 2(3), 136–147. https://doi.org/10.22105/cand.2023.191008
[11]   Edalatpanah, S. A., Komazec, N., & Pamucar, D. (2024). Making two-channel pricing decisions in a multi-objective closed-loop supply chain network under uncertainty considering reliability (Case study: Steel industry). Big data and computing visions, 4(3), 201–218.
[12]  Letafat, F., Gholamian, M. R., & Arabi, M. (2024). Designing a reliable supply chain network for perishable crops considering the risk of disruption (Case study: Tomato supply chain). Journal of decisions and operations research, 9(3), 666–689. https://doi.org/10.22105/dmor.2024.419272.1797
[13]  ValizadehDizaj, Z., Fazlzadeh, A., Ahmadian, V., & Nagdi, S. (2025). Investigating the role of dynamic capability and supply chain resilience on companies' financial performance during disruptions. Innovation management and operational strategies. https://doi.org/10.22105/imos.2025.501248.1429
[14]  Carter, C. R., & Liane Easton, P. (2011). Sustainable supply chain management: Evolution and future directions. International journal of physical distribution & logistics management, 41(1), 46–62. https://doi.org/10.1108/09600031111101420
[15] Rakshit, I. (2025). AI-driven cloud solutions for smart city data analytics. Systemic analytics, 3(1), 27–34. https://doi.org/10.31181/sa31202540
[16]  Nagurney, A., Liu, Z., & Woolley, T. (2007). Sustainable supply chain and transportation networks. International journal of sustainable transportation, 1(1), 29–51. https://doi.org/10.1080/15568310601060077
[17]  Sazvar, Z., Sepehri, M., & Baboli, A. (2016). A multi-objective multi-supplier sustainable supply chain with deteriorating products, case of cut flowers. IFAC-papersonline, 49(12), 1638–1643. https://doi.org/10.1016/j.ifacol.2016.07.815
[18]  Fahimnia, B., Sarkis, J., & Eshragh, A. (2015). A tradeoff model for green supply chain planning: A leanness-versus-greenness analysis. Omega (United Kingdom), 54, 173–190. https://doi.org/10.1016/J.OMEGA.2015.01.014
[19]  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
[20]  Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1–20. 10.1016/j.fss.2012.04.010
[21]  Biuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of cleaner production, 260, 120842. https://doi.org/10.1016/j.jclepro.2020.120842
[22]  Gholizadeh, H., & Fazlollahtabar, H. (2020). Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry. Computers & industrial engineering, 147, 106653. https://doi.org/10.1016/j.cie.2020.106653
[23]  Garg, K., Kannan, D., Diabat, A., & Jha, P. C. (2015). A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. Journal of cleaner production, 100. 297-314 . https://doi.org/10.1016/j.jclepro.2015.02.075
[24]  Wang, J., & Wan, Q. (2022). A multi-period multi-product green supply network design problem with price and greenness dependent demands under uncertainty. Applied soft computing, 114, 108078. https://doi.org/10.1016/j.asoc.2021.108078
[25]  Golpîra, H., & Javanmardan, A. (2022). Robust optimization of sustainable closed-loop supply chain considering carbon emission schemes. Sustainable production and consumption, 30, 640–656. https://doi.org/10.1016/j.spc.2021.12.028
[26]  Sadeghi Rad, R., & Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of cleaner production, 196, 1549–1565. https://doi.org/10.1016/j.jclepro.2018.06.034
Journal of Quality Engineering and Management
Volume 15, Issue 1
Spring 2025
Pages 31-49

Home

Editorial Board

Indexing and Abstracting

Guide for Authors

Submit Manuscript

Reviewers

Contact Us