当前位置: X-MOL 学术Soft Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models
Soft Computing ( IF 3.1 ) Pub Date : 2020-04-02 , DOI: 10.1007/s00500-020-04904-w
Gazi Murat Duman , Elif Kongar , Surendra M. Gupta

Abstract

Growing rates of innovation and consumer demand resulted in rapid accumulation of waste of electrical and electronic equipment or electronic waste (e-waste). In order to build and sustain green cities, efficient management of e-waste rises as a viable response to this accumulation. Accurate e-waste predictions that municipalities can utilize to build appropriate reverse logistics infrastructures gain significance as collecting, recycling and disposing the e-waste become more complex and unpredictable. In line with its significance, the related literature presents several methodologies focusing on e-waste generation forecasting. Among these methodologies, grey modeling approach has aroused interest due to its ability to present meaningful results with small-sized or limited data. In order to improve the overall success rate of the approach, several grey modeling-based forecasting techniques have been proposed throughout the past years. The performance of these models, however, profoundly leans on the parameters used with no established consensus regarding the suitable criteria for better accuracy. To address this issue and to provide a guideline for academicians and practitioners, this paper presents a comparative analysis of most utilized grey modeling methods in the literature improved by particle swarm optimization. A case study employing e-waste data from Washington State is provided to demonstrate the comparative analysis proposed in the study.



中文翻译:

反向物流操作中电子废物的预测分析:改进的单变量灰色模型的比较

摘要

创新和消费者需求的增长导致电气和电子设备废物或电子废物(电子废物)的迅速积累。为了建设和维持绿色城市,对电子垃圾进行有效管理是对这种积累的一种可行的回应。随着电子废物的收集,回收和处置变得更加复杂和不可预测,市政当局可以用来建立适当的逆向物流基础设施的准确电子废物预测变得越来越重要。根据其重要性,相关文献提出了几种关注电子废物产生预测的方法。在这些方法中,由于灰色建模方法能够以小尺寸或有限的数据呈现有意义的结果,因此引起了人们的兴趣。为了提高该方法的整体成功率,在过去的几年中,已经提出了几种基于灰色建模的预测技术。但是,这些模型的性能极大地依赖于所使用的参数,而对于更好的准确度的合适标准,尚无公认的共识。为了解决此问题并为院士和从业人员提供指导,本文对通过粒子群优化改进的文献中最常用的灰色建模方法进行了比较分析。提供了一个使用华盛顿州电子废物数据的案例研究,以证明该研究中提出的比较分析。完全依赖于所使用的参数,而就合适的标准而言,尚无公认的共识,以获得更好的准确性。为了解决此问题并为院士和从业人员提供指导,本文对通过粒子群优化改进的文献中最常用的灰色建模方法进行了比较分析。提供了一个使用华盛顿州电子废物数据的案例研究,以证明该研究中提出的比较分析。完全依赖于所使用的参数,而就合适的标准而言,尚无公认的共识,以获得更好的准确性。为了解决此问题并为院士和从业人员提供指导,本文对通过粒子群优化改进的文献中最常用的灰色建模方法进行了比较分析。提供了一个使用华盛顿州电子废物数据的案例研究,以证明该研究中提出的比较分析。

更新日期:2020-04-03
down
wechat
bug