This paper aims to better manage the reverse supply chain of the automotive industry in the context of green, circular, and sustainable development by predicting the number of end-of-life vehicles to be recycled through the establishment of a multi-factor model. The prediction of the number of end-of-life vehicles to be recycled in this paper will support the end-of-life vehicle recycling industry in terms of recycling management and investment decision-making and provide a reference for the formulation and implementation of policies relating to end-of-life vehicles. To solve the problems posed by nonlinear characteristics and uncertainty in the number of end-of-life vehicles recycled, and deal with the multiple factors influencing the recycling number, this paper presents a combined prediction model consisting of a grey model, exponential smoothing and an artificial neural network optimized by the particle swarm optimization (PSO) algorithm. Using Shanghai's end-of-life vehicle reverse logistics industry as an example, this study selects historical data about end-of-life vehicles recycled in Shanghai during the 2005–2016 period, identifies multiple influential factors, and validates the effectiveness and feasibility of the prediction model through empirical research. This paper proposes an effective prediction model for end-of-life vehicle industry managers, researchers, and regulators dealing with the industry's common challenges.

本文旨在通过建立多因素模型来预测将要回收的报废汽车的数量，从而在绿色，循环和可持续发展的背景下更好地管理汽车行业的逆向供应链。本文对报废汽车回收数量的预测将为报废汽车回收行业的回收管理和投资决策提供支持，并为政策的制定和实施提供参考。有关报废车辆的信息。为解决报废车辆报废数量的非线性特征和不确定性所带来的问题，并解决影响报废车辆数量的多种因素，本文提出了一种由灰色模型组成的组合预测模型，粒子群优化（PSO）算法优化了指数平滑和人工神经网络。以上海的报废汽车逆向物流行业为例，本研究选择了2005-2016年期间在上海回收的报废汽车的历史数据，确定了多个影响因素，并验证了该方法的有效性和可行性。通过实证研究建立预测模型。本文为报废汽车行业的经理，研究人员和监管机构提出了有效的预测模型，以应对该行业的共同挑战。本研究选择了2005年至2016年期间在上海回收的报废汽车的历史数据，确定了多个影响因素，并通过实证研究验证了该预测模型的有效性和可行性。本文为报废汽车行业的经理，研究人员和监管机构提出了有效的预测模型，以应对该行业的共同挑战。本研究选择了2005年至2016年期间在上海回收的报废汽车的历史数据，确定了多个影响因素，并通过实证研究验证了该预测模型的有效性和可行性。本文为报废汽车行业的经理，研究人员和监管机构提出了有效的预测模型，以应对该行业的共同挑战。