Analysis of Sustainable Recycling Logistics via Multi-Agent Artificial Intelligence Decoded Business Analytics

Recycling projects powered by clean logistics face the fundamental challenge of improving performance while balancing environmental sustainability, technological change, and economic feasibility. Although multiple performance dimensions influence these projects, existing studies rarely provide a structured approach to identify which criteria and investment strategies should be prioritized under uncertainty. This lack of integrated prioritization frameworks represents a major gap in the literature. The purpose of this study is to develop a robust decision-making model that systematically evaluates performance criteria and strategic alternatives for clean logisticsbased recycling projects. To address this gap, the proposed model integrates consensus-based expert selection with Manhattan distance-based centrality, cognitive maps for criterion weighting, orthogonal metric robust aggregation for strategy ranking, and newly developed Chipper fuzzy sets to handle uncertainty. The model offers methodological advantages by capturing expert consensus, causal relationships, and robust aggregation within a unified framework. The results indicate that technology adaptability and costs are the most influential criteria, while electric fleet operators and recycling park developers emerge as the most effective investment strategies. These findings suggest prioritizing flexible technologies and integrated low-emission infrastructure to enhance the long-term performance and sustainability of recycling projects powered by clean logistics.