Supplier selection and evaluation model based on the adaptive genetic algorithm and BP neural network

It is important for an enterprise to select one or more trusty suppliers to be the partners and build an efficient supply chain in the fierce competition. A selection and evaluation model based on the adaptive genetic algorithm and improved BP neural network is created in the paper. In the former process, the adaptive genetic algorithm is applied to adjust weights and thresholds of the BP neural network, and the additional momentum BP algorithm with learning rate adaptive adjustable method is used to search in neighborhoods of the approximate optimal solution in the later. The program written in visual basic 6.0, and the results show that the learning precision of the combination algorithm is more correctly than that of the generally BP algorithm. The training speed and convergence rate of the former is significantly improved in the experiment. It is helpful to realize automated evaluation for supplier selection and evaluation.