Multi-echelon inventory optimization of waste electrical and electronic equipment closed-loop supply chain based on reinforcement learning under carbon tax policy

In response to environmental challenges posed by waste electrical and electronic equipment (WEEE), the WEEE closed-loop supply chain (CLSC) has emerged as a crucial means to promote circular economy through the recycling and reuse of WEEE, which not only reduces waste emissions but also improves the efficiency of resource utilization. In practice, both economic and environmental benefits must be considered in sustainable manufacturing within the CLSC to ensure the sustainable development of enterprises. Therefore, a multi-echelon, multi-period inventory model for the CLSC under carbon tax policy is developed in this paper, which innovatively introduces the carbon footprint to assess environmental impact and integrates the impacts of collection planning and the uncertainty of recycling quantity and product demand on the system, aiming to minimize total enterprise costs. To address the uncertainties in the model, the Proximal Policy Optimization (PPO) algorithm is employed to train a reinforcement learning (RL) agent. This agent enables enterprises to dynamically adjust internal strategies such as collection and production, as well as external procurement strategies, based on inventory levels and market conditions. By internalizing carbon emission costs through the carbon tax rate, the RL agent optimizes total costs while achieving a balance between economic and environmental benefits. Numerical experiments demonstrate that the PPO algorithm outperforms the traditional inventory management policy in terms of both cost control and carbon footprint reduction. Moreover, the moderate carbon tax policy on the CLSC appropriately increases the cost of enterprises while significantly reducing their carbon footprint and promoting sustainable development.