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Title Supporting mobile WEEE collection on demand: A method for multi-criteria vehicle routing, loading and cost optimisation
ID_Doc 13959
Authors Nowakowski, P; Król, A; Mrówczynska, B
Title Supporting mobile WEEE collection on demand: A method for multi-criteria vehicle routing, loading and cost optimisation
Year 2017
Published
Abstract The task of increasing collection rates of waste electrical and electronic equipment (WEEE) is an important challenge in the global economy, and especially in the European Union where stiffer collection targets set out in a new WEEE directive are to be effective by 2019. As the circular economy approach replaces the linear model, resource recycling activities become a priority in waste management policy. As new techniques and possibilities of waste collection systems emerge, opportunities are created for improving efficiency for collection companies and affording benefits for the environment. A model proposed for mobile WEEE collection in this study considers a multi-criteria approach in developing a cost efficient method for pick up on demand from residents or electrical and electronic equipment (EEE) stores. The algorithm used in this model optimises vehicle routes and helps in selecting a number of vehicles from a heterogeneous fleet, incorporating the WEEE loading problem. Using genetic algorithm and fuzzy logic, this model optimises costs and resources required to complete the WEEE collection assuring timely pick up of the waste equipment. The numerical model is verified in a case study in Opole, a city in the south of Poland. The results show that the proposed model can handle the multiple parameter optimisation problem including operational costs, efficient use of vehicles from a fleet, efficient waste loading in vehicles and residents' satisfaction with timely pick up of the waste equipment from a household. Such system can be successfully applied even for large cities. The algorithm provides an opportunity for writing software or mobile apps design to be used by WEEE collection companies. (C) 2017 Elsevier Ltd. All rights reserved.
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