A grey genetic algorithm for uncertainty reverse logistics

This study presents the uncertainty remanufacturing demand (URD) for green suitcase chain to predict the return demand model of an flexible inventory model. In this research, we proposed reverse production design by green prediction model for the division of green cost responsibility. This philosophy have become a popular topic that improved the effectiveness of extended producer responsibility for green supply chain management (GSCM). It is different from the traditional division of green cost responsibility processes that we proposed a novel measurement by the uncertainty index from green prediction model evolutions. A grey genetic algorithm (GGA) was designed by adaptive designs for URD optimization. These designs provided a novel evaluation index by varying all variables to achieve the global optimization of green cost responsibility. The new demand prediction design derived from the crossover and mutation rate of an adjusted GA search optimization. This research verified these methodologies in a practical case. The experiment is simulated by a GGA to reach an optimal solution.