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Erschienen in:
01.07.2014 | Original Article
Network site optimization of reverse logistics for E-commerce based on genetic algorithm
Erschienen in: Neural Computing and Applications | Ausgabe 1/2014
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Abstract
Product returns have been viewed as an unavoidable cost for online sales, forfeiting any chance of cost savings. As competition pressure continues to mount in E-commerce, B2B or B2C, E-commerce corporations have begun to explore the possibility of managing product returns in a more cost-efficient manner by rescheduling the locations of recycling centers. However, few studies have addressed the problem of optimization of the numbers and location of collecting points, recycling centers and remanufacturing centers for E-commerce based on genetic algorithm. To fill the void in such a line of research, this paper proposes a genetic algorithm-based model that can solve the location-allocation optimization of reverse logistics for E-commerce. The usefulness of the proposed model and algorithm was validated by its application to an illustrative example dealing with logistics sites allocation from online sales.