Designing Resilient Biomass Energy Supply Networks under Environmental Disruptions and Market Uncertainty: An Integrated Data-Driven Clustering, MCDM, and Bi-Level Optimization Framework

Authors

  • Amirhossein Amou Jafari * Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
  • Kiana Salehi Department of Industrial Engineering, University of Tehran, Tehran, Iran.

https://doi.org/10.48313/scodm.v3i2.58

Abstract

Given the growing energy demand, limitations of fossil resources, and the urgent need to mitigate environmental impacts, developing renewable energy supply networks, especially those based on biomass, has gained significant importance. Accordingly, this study proposes a hybrid multi-phase decision-making framework for the optimal design of a resilient biomass energy supply network under environmental and market uncertainties. In the first phase, spatial data from 120 agricultural sites across eight provinces of Iran were clustered into homogeneous groups using four data-driven algorithms, namely Constrained K-Means (CKM), the Gaussian Mixture Model (GMM), Spectral Clustering, and Affinity Propagation (AP). Subsequently, Bayesian Best-Worst and Best-Worst PROMETHEE methods were employed to weight and rank decision criteria, facilitating the identification of optimal hub locations. The resulting clusters and hub scores were then incorporated as input parameters for the network design problem: in the first phase, hub location and cluster-to-hub allocation were determined; in the second phase, a bi-level, multi-period mathematical model was developed to optimize network flows and intra-cluster routing under uncertainty. Numerical results reveal that employing Spectral Clustering led to a 28% reduction in total supply chain costs compared to the uncluttered baseline, with other algorithms achieving reductions between 10% and 19%. This research, grounded in real-world data collected from eight provinces, demonstrates the effectiveness of data-driven and multi-criteria approaches in enhancing the sustainability, cost-efficiency, and resilience of biomass energy supply networks.

Keywords:

Biomass supply chain, Data-driven clustering, Multi-criteria decision-making, Network optimization, Hub location, Uncertainty modeling

References

  1. [1] Abandansari, S. A. M., Ghasemi, P., Attar, A., Goodarzian, F., & Ahmadi, S. A. (2025). A hybrid biomass supply chain optimization approach using sequential adaptive fuzzy inference learning: Insights from a cogeneration system in Europe. Results in engineering, 26(2), 105261. https://doi.org/10.1016/j.rineng.2025.105261
  2. [2] Nunes, L. J., Casau, M., Dias, M. F., Matias, J. C. O., & Teixeira, L. C. (2023). Agroforest woody residual biomass-to-energy supply chain analysis: Feasible and sustainable renewable resource exploitation for an alternative to fossil fuels. Results in engineering, 17, 101010. https://doi.org/10.1016/j.rineng.2023.101010
  3. [3] Murele, O. C., Zulkafli, N. I., Kopanos, G., Hart, P., & Hanak, D. P. (2020). Integrating biomass into energy supply chain networks. Journal of cleaner production, 248, 119246. https://doi.org/10.1016/j.jclepro.2019.119246
  4. [4] Petridis, K., Grigoroudis, E., & Arabatzis, G. (2018). A goal programming model for a sustainable biomass supply chain network. International journal of energy sector management, 12(1), 79–102. https://doi.org/10.1108/IJESM-09-2017-0002
  5. [5] Allegranzi, B., Tartari, E., & Pittet, D. (2021). “Seconds save lives—clean your hands”: the 5 May 2021 World Health Organization SAVE LIVES: Clean Your Hands campaign. Antimicrobial resistance & infection control, 10(1), 55. https://doi.org/10.1186/s13756-021-00926-7
  6. [6] Fattahi, M., Govindan, K., & Farhadkhani, M. (2021). Sustainable supply chain planning for biomass-based power generation with environmental risk and supply uncertainty considerations: A real-life case study. International journal of production research, 59(10), 3084–3108. https://doi.org/10.1080/00207543.2020.1746427
  7. [7] Zhang, D., Wang, J., Li, S., & Wang, Y. (2026). Resilient agricultural biomass supply chain network design with uncertain disruptions: A data-driven globalised robust optimisation approach. International journal of production research, 1–27. https://doi.org/10.1080/00207543.2026.2647006
  8. [8] Nguyen, D. D., Nananukul, N., & Luukka, P. (2026). Multi-objective fuzzy optimization model for a biomass-to-electricity supply chain network design under uncertainty. Renewable energy focus, 58, 100856. https://doi.org/10.1016/j.ref.2026.100856
  9. [9] Grigoroudis, E., Petridis, K., & Arabatzis, G. (2014). RDEA: A recursive DEA based algorithm for the optimal design of biomass supply chain networks. Renewable energy, 71, 113–122. https://doi.org/10.1016/j.renene.2014.05.001
  10. [10] Ghani, N. M. A. M. A., Vogiatzis, C., & Szmerekovsky, J. (2018). Biomass feedstock supply chain network design with biomass conversion incentives. Energy policy, 116, 39-49. https://doi.org/10.1016/j.enpol.2018.01.042
  11. [11] Rasekh, A., Hamidzadeh, F., Sahebi, H., & Pishvaee, M. S. (2023). A sustainable network design of a hybrid biomass supply chain by considering the water–energy–carbon nexus. Energy science and engineering, 11(3), 1107–1132. https://doi.org/10.1002/ese3.1374
  12. [12] Bhatt, G., Upadhyay, A., & Sahoo, K. (2025). Biomass supply chain network design: Integrating fixed and portable preprocessing depots for cost efficiency and sustainability. Applied energy, 389, 125757. https://doi.org/10.1016/j.apenergy.2025.125757
  13. [13] Wang, Z., You, Y., Wang, Z., & Wang, H. (2025). Optimization of the agricultural and forestry biomass power generation supply chain considering multi-period inventory. Sustainable futures, 10, 100831. https://doi.org/10.1016/j.sftr.2025.100831
  14. [14] Azad, A. A. S., Oishi, Z. T., Haque, M. A., Das, P., Udoy, S. A., & Bhuiya, K. M. S. (2024). An integrated framework for assessing renewable-energy supply chains using multicriteria decision-making: A study on Bangladesh. Clean energy, 8(3), 1-19. https://doi.org/10.1093/ce/zkae019
  15. [15] Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega (United Kingdom), 96, 102075. https://doi.org/10.1016/j.omega.2019.06.001
  16. [16] Ishizaka, A., & Resce, G. (2021). Best-Worst PROMETHEE method for evaluating school performance in the OECD's PISA project. Socio-economic planning sciences, 73, 100799. https://doi.org/10.1016/j.seps.2020.100799

Downloads

Published

2026-06-08

Issue

Vol. 3 No. 2 (2026): Supply Chain and Operations Decision Making

Section

Articles

How to Cite

Amou Jafari, A., & Salehi, K. . (2026). Designing Resilient Biomass Energy Supply Networks under Environmental Disruptions and Market Uncertainty: An Integrated Data-Driven Clustering, MCDM, and Bi-Level Optimization Framework. Supply Chain and Operations Decision Making, 3(2), 144-155. https://doi.org/10.48313/scodm.v3i2.58

Similar Articles

1-10 of 39

You may also start an advanced similarity search for this article.