Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey model
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METHODOLOGIES AND APPLICATION
Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models Gazi Murat Duman1 • Elif Kongar2 • Surendra M. Gupta3
Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Electronic waste Fractional order Improved grey modeling Particle Swarm Optimization Reverse logistics Rolling mechanism
1 Introduction
Communicated by V. Loia. & Gazi Murat Duman [email protected] Elif Kongar [email protected] Surendra M. Gupta [email protected] 1
Trefz School of Business, University of Bridgeport, 230 Park Avenue, Mandeville Hall, Room 22B, Bridgeport, CT 06604, USA
2
Departments of Mechanical Engineering and Technology Management, University of Bridgeport, 221 University Avenue, School of Engineering, 141 Technology Building, Bridgeport, CT 06604, USA
3
Department of Mechanical and Industrial Engineering, Northeastern University, 334 Snell Engineering Center, 360 Huntington Avenue, Boston, MA 02115, USA
Increasing rate of new product introduction coupled with the rising consumer demand has shortened the expected life span of electrical and electronic equipment. Because of the disruptive nature of new electronic products, every time a new product is introduced into the market, one or more products are likely to become obsolete. Thus, management of electronic waste (e-waste) or waste of electrical and electronic equipment (WEEE) has become a major challenge for the US municipal governments. C
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