Optimization of the Supply Chain Production Planning Programming under Hybrid Uncertainties
The uncertain parameters of the supply chain production planning system mainly include the customer demand, unit product revenue, lead time and the material prices. The uncertainties may have complicated characteristics. The fuzzy grey variable was proposed to describe some uncertain parameters containing fuzzy and grey two-fold uncertain factors. The uncertain programming model of supply chain production planning system was presented under fuzzy and grey uncertain conditions. The designed fuzzy grey simulation technology and the grey simulation technology can generate input-output data to approximate the uncertain functions on the basis of the credibility measure and the chance measure of fuzzy grey variables. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved particle swarm optimization algorithm based on the differential evolution algorithm can optimize the uncertain programming models. One numerical example is given to illustrate the effectiveness of the designed model and algorithm.