Uncertain programming of the distribution/recovery center in forward/reverse hybrid logistics system

The distribution/recovery center distributes the manufactured and remanufactured products to different demand areas, also receives and transports the return products to the remanufacturing center. Remanufacturing the return products can improve the customer service level and reduce the supply chain cost. The uncertain programming model of the integrated forward/reverse logistics system was proposed under the grey fuzzy uncertainty and grey uncertainty. The proposed grey fuzzy simulation methodology can generate input-output data for the uncertain functions. The designed neural network trained from the input-output data can expedite the simulation process of the uncertain functions. The hybrid intelligent optimization algorithm integrating the grey simulation, grey fuzzy simulation, neural network and the genetic algorithm can optimize the uncertain programming model. One numerical example is given to illustrate the effectiveness of the proposed model and algorithm.