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

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.