Development of a mathematical pricing model in a closed-loop green supply chain under different customer behaviors

This study examines pricing strategies in a closed-loop green supply chain that integrates forward and reverse logistics to strike a balance between economic profitability and environmental sustainability. By addressing customer segmentation and dynamic market behaviors, this research highlights the challenges of developing effective pricing mechanisms that respond to diverse customer preferences, including price sensitivity, environmental awareness, and willingness to engage in reverse logistics. The proposed model integrates dynamic cost factors such as demand elasticity, life cycle costs, and sustainability rewards to optimize pricing strategies. Using k-means clustering, customers are divided into price-sensitive, value-oriented, and sustainability-focused groups, which enables customized pricing approaches. A dynamic pricing model, supported by sensitivity analysis and visualization techniques, assesses the impact of market fluctuations on pricing decisions. This comprehensive methodology provides practical insights for designing adaptive and sustainable pricing models that align with circular economy goals. By bridging the gap between profitability and environmental responsibility, this research contributes to the development of sustainable supply chain practices that meet modern business and ecological needs.