Forecasting the number of end-of-life vehicles using a hybrid model based on grey model and artificial neural network
Forecasting the number of end-of-life vehicles using a hybrid model based on grey model and artificial neural network
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DOI：
10.1016/j.jclepro.2018.08.176
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发表时间：
2018-11-20
影响因子：
11.1
通讯作者：
Zhang, Ji
中科院分区：
文献类型：
--
作者：
Hao, Hao;Zhang, Qian;Zhang, Ji
本文通过建立多因素模型对报废汽车回收数量进行预测，以期在绿色、循环、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在回收管理和投资决策方面为报废汽车回收行业提供支持，为制定和实施与报废汽车相关的政策提供参考。为解决报废汽车回收数量的非线性和不确定性问题，处理影响回收数量的多种因素，提出了一种由灰色模型、指数平滑和粒子群算法优化的人工神经网络组成的组合预测模型。以上海市报废汽车逆向物流行业为例，选取2005-2016年上海报废汽车回收的历史数据，识别多种影响因素，并通过实证研究验证预测模型的有效性和可行性。本文提出了一个有效的预测模型，以供报废汽车行业的管理人员、研究人员和监管机构应对该行业的共同挑战。(C)2018爱思唯尔有限公司。保留所有权利。
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. (C) 2018 Elsevier Ltd. All rights reserved.