Resumen

SEIDI, M.  y  KIMIAGARI, A.M.. A hybrid genetic algorithm-neural network approach for pricing cores and remanufactured cores. S. Afr. J. Ind. Eng. [online]. 2010, vol.21, n.2, pp.131-148. ISSN 2224-7890.

Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. Remanufacturing is an industrial process that makes used products reusable. One of the important aspects in both reverse logistics and remanufacturing is the pricing of returned and remanufactured products (called cores). In this paper, we focus on pricing the cores and remanufactured cores. First we present a mathematical model for this purpose. Since this model does not satisfy our requirements, we propose a simulation optimisation approach. This approach consists of a hybrid genetic algorithm based on a neural network employed as the fitness function. We use automata learning theory to obtain the learning rate required for training the neural network. Numerical results demonstrate that the optimal value of the acquisition price of cores and price of remanufactured cores is obtained by this approach.

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