A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns

By Hokey Min, H. Ko, C. Ko. 2006

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Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. (2010, background): “Min et al. (2006a) present a non-linear integer program for solving the multi echelon RL problem involving product returns without considering temporal consolidation issues in a multiple planning horizon.”
Artificial intelligence in supply chain management: theory and applications (2010, background): “…container loading (Gehring and Bortfeldt 1997, Bortfeldt and Gehring 2001); material handling (Wu and Appleton 2002), delivery reliability assurance (Antony et al. 2006); freight consolidation (Min et al. 2006a, b); supplier selection (Rao 2007); and express courier services (Ko et al. 2007).”
Green Supply-Chain Management: A State-of-the-Art Literature Review (2007, methodology): “Min et al. (2006) determine the number and location of centralized return centres using a non-linear mixed-integer programming model and a genetic algorithm that solves the RL problem involving product returns.”
A closed-loop logistic model with a spanning-tree based genetic algorithm (2010, methodology): “[24] also successfully used GA to develop a multi-echelon reverse logistics network for product returns.”
E-fulfillment and multi-channel distribution - A review (2006, methodology): “Therefore, designing efficient return processes is of prime importance (Min et al. 2006).”