A stochastic programming approach with improved multi-criteria scenario-based solution method for sustainable reverse logistics design of waste electrical and …

H Yu, WD Solvang - Sustainability, 2016 - mdpi.com
Sustainability, 2016mdpi.com
Today, the increased public concern about sustainable development and more stringent
environmental regulations have become important driving forces for value recovery from end-
of-life and end-of use products through reverse logistics. Waste electrical and electronic
equipment (WEEE) contains both valuable components that need to be recycled and
hazardous substances that have to be properly treated or disposed of, so the design of a
reverse logistics system for sustainable treatment of WEEE is of paramount importance. This …
Today, the increased public concern about sustainable development and more stringent environmental regulations have become important driving forces for value recovery from end-of-life and end-of use products through reverse logistics. Waste electrical and electronic equipment (WEEE) contains both valuable components that need to be recycled and hazardous substances that have to be properly treated or disposed of, so the design of a reverse logistics system for sustainable treatment of WEEE is of paramount importance. This paper presents a stochastic mixed integer programming model for designing and planning a generic multi-source, multi-echelon, capacitated, and sustainable reverse logistics network for WEEE management under uncertainty. The model takes into account both economic efficiency and environmental impacts in decision-making, and the environmental impacts are evaluated in terms of carbon emissions. A multi-criteria two-stage scenario-based solution method is employed and further developed in this study for generating the optimal solution for the stochastic optimization problem. The proposed model and solution method are validated through a numerical experiment and sensitivity analyses presented later in this paper, and an analysis of the results is also given to provide a deep managerial insight into the application of the proposed stochastic optimization model.
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