An improved evaluation model for supplier selection based on particle swarm optimisation-back propagation neural network

With the trend of supply chain globalisation, competition among enterprises is becoming more intense. Enterprises urgently need to improve their core competitiveness, and the enhancement of the competencies can depend on technologies services and the quality of suppliers. Since external factors are less controllable, this study starts with the quality of suppliers and proposes a supplier evaluation method that combines particle swarm optimisation with neural network algorithm to maximise the interests of enterprises. The particle swarm algorithm to lock the approximate location of the global optimum is first employed. Based on this, we establish an evaluation model of suppliers to train for the minimum errors between the desired and predicted values by constructing a back propagation (BP) neural network. Finally, the output results of the proposed method is compared with the BP neural network without the particle swarms optimisation. The proposed model is less empirically sensitive to the initialisation and can quickly converge to the local optimums, which overcomes the shortage of traditional neural networks and is more applicable to supplier evaluation.