Optimizing Sustainable Green Logistics with AI-Enhanced RAM-Hybrid Models: Addressing Economic and Crisis Management Challenges

Subject Areas : Mathematical Optimization

Robabeh Eslami ¹ , Mohammad Khoveyni ²

1 - Department of Mathematics, ST.C., Islamic Azad University, Tehran, Iran
2 - Department of Mathematics, YI.C., Islamic Azad University, Tehran, Iran

Received: 2025-03-24 Accepted : 2025-05-10 Published : 2025-03-17

Keywords: Artificial Intelligence, Machine Learning Models, Data Envelopment Analysis (DEA), RAM-Hybrid Model, Green Logistics and Crisis Logistics, Economic and Managerial Impacts,

Abstract :

This paper explores the integration of green logistics and crisis logistics, two critical areas in modern transportation management. While crisis logistics ensures the timely delivery of goods during emergencies, green logistics focuses on minimizing environmental impacts. Traditional methods for evaluating logistics efficiency often fail to capture the complexity of real-world challenges and overlook economic and managerial considerations. To address these gaps, this study proposes a hybrid approach combining the Range-Adjusted Measure (RAM) model with artificial intelligence (AI) techniques within the framework of Data Envelopment Analysis (DEA). The RAM model, known for its non-radial efficiency evaluation, is enhanced with AI algorithms to optimize input and output weights, improving analytical accuracy. By incorporating budgetary constraints, this approach enables a more precise assessment of green logistics efficiency while considering financial limitations. The study introduces a novel DEA-AI model that evaluates environmental and economic performance across industries. AI-driven optimization refines cost and workforce inputs while maximizing outputs such as carbon reduction and delivery speed, ultimately enhancing decision-making and competitiveness. The research further demonstrates how AI enhances the efficiency of decision-making units (DMUs) in transportation and crisis management. By fine-tuning model parameters, AI improves logistical efficiency, reduces costs, and strengthens financial performance. A case study is presented to validate the model’s effectiveness compared to traditional methods. This study highlights the importance of hybrid models in optimizing logistical systems, showcasing how AI-powered efficiency analysis can drive sustainable and economically viable logistics operations.

References:

[1] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
[2] Yang, G., Zhou, P., & Wang, J. (2022). Efficiency assessment in green logistics using DEA. Journal of Cleaner Production, 34(5), 1021-1032. https://doi.org/10.1016/j.jclepro.2021.128071
[3] Wang, Z., & Liu, L. (2023). Application of AI and Machine Learning in Green Logistics for Crisis and Disaster Management. International Journal of Logistics Research and Applications, 26(2), 200-215. https://doi.org/10.1080/13675567.2023.1868042
[4] Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509. https://doi.org/10.1016/S0377-2217(99)00407-5
[5] Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Models for the estimation of technical and scale inefficiencies in DEA. Management Science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
[6] Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on Data Envelopment Analysis. Springer Science & Business Media. https://doi.org/10.1007/978-1-4419-6151-8
[7] Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16-20. https://doi.org/10.1016/S0377-2217(01)00293-4
[8] Sarkis, J., & Zhu, Q. (2018). Green supply chain management: A decade of studies. International Journal of Production Economics, 182(1), 231-239. https://doi.org/10.1016/j.ijpe.2016.08.042
[9] Färe, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontiers. Cambridge University Press. https://doi.org/10.1017/CBO9780511664114
[10] Cook, W. D., Tone, K., & Zhu, J. (2014). Data Envelopment Analysis: Prior to choosing a model. Omega, 44, 1-4. https://doi.org/10.1016/j.omega.2013.09.004
[11] Li, Z., & Lin, B. (2021). Efficiency evaluation of green logistics in China using DEA with undesirable outputs. Environmental Science and Pollution Research, 28(1), 213-224. https://doi.org/10.1007/s11356-020-10511-w
[12] Yang, F., Sun, X., & Zhang, Y. (2020). AI-powered optimization in sustainable logistics. Journal of Cleaner Production, 245, 118827. https://doi.org/10.1016/j.jclepro.2019.118827
[13] He, Y., Wang, S., & Liu, W. (2018). Incorporating machine learning with DEA for efficiency improvement. Expert Systems with Applications, 94, 115-127. https://doi.org/10.1016/j.eswa.2017.10.028
[14] Tsai, W., & Hung, S. J. (2009). A fuzzy goal programming approach to the green supply chain optimization problem. International Journal of Production Research, 47(24), 6849-6866. https://doi.org/10.1080/00207540802680655
[15] Hsu, C.-C., & Hu, A. H. (2008). Green supply chain management in the electronic industry. International Journal of Environmental Science and Technology, 5(2), 205-216. https://doi.org/10.1007/BF03326011
[16] Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A. A. (2024). Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review. Sustainability, 16(21), 9145. https://doi.org/10.3390/su16219145
[17] Malhotra, G., & Kharub, M. (2024). Elevating logistics performance: harnessing the power of artificial intelligence in e-commerce. The International Journal of Logistics Management, 36(1), 290-321. https://doi.org/10.1108/ijlm-01-2024-0046
[18] Alzoubi, Y. I. (2025). AI-Powered Reverse Logistics: A Pathway to Sustainable Supply Chains. In International Conference on Advanced Network Technologies and Intelligent Computing, 2335, (pp. 384-402), Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-83793-7_25
[19] Keramati, A., Apornak, A., Abedi, H., Otrodi, F., & Roudneshin, M. (2018). The effect of service recovery on customers' satisfaction in e-banking: an empirical investigation. International Journal of Business Information Systems, 29(4), 459-484. https://doi.org/10.1504/IJBIS.2018.096033
[20] Mousavi, S. F., Apornak, A., & Pourhassan, M. (2023). Robust optimization model to improve supply chain network‎ productivity under uncertainty. Journal of applied research on industrial engineering, 10(2), 273-285. https://doi.org/10.22105/jarie.2022.316357.1402
[21] Sun, J. - Y., Sun, J., Apornak, A., Keramati, A., Ma, G., & Ma, G. (2024). Presenting a mathematical model for reduction of delays in construction projects considering quality management criteria in uncertainty conditions. Journal of Engineering Research, 12(3), 476-483. https://doi.org/10.1016/j.jer.2023.08.021
[22] Liu, W., Chen, X., & Zhang, S. (2022). Electric vehicle optimization in green logistics. Transportation Research Part D: Transport and Environment, 102, 103118. https://doi.org/10.1016/j.trd.2021.103118
[23] Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management, 37(2), 99-114. https://doi.org/10.1108/09600030710734820
[24] Khalili-Damghani, K., Noorzadeh, F., & Apornak, A. (2022). Resource allocation problems in data envelopment analysis with simultaneous shared costs and common revenue. International Journal of Data Envelopment Analysis, 10(2). https://doi.org/10.30495/ijdea.2022.66147.1153
[25] Rane, N., Choudhary, S. P., & Rane, J. (2024). Artificial intelligence and machine learning for resilient and sustainable logistics and supply chain management. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4847087
[26] Castillo, V. E., & González, F. J. (2021). AI and green logistics: Transforming efficiency in the era of sustainability. Journal of Business Research, 129, 123-135. https://doi.org/10.1016/j.jbusres.2020.12.037
[27] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/front_matter.pdf
[28] Chollet, F. (2017). Deep Learning with Python. Manning Publications.
[29] Toth, P., & Vigo, D. (2002). The Vehicle Routing Problem. Society for Industrial and Applied Mathematics (SIAM), Philadelphia. http://dx.doi.org/10.1137/1.9780898718515