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Aiming at solving the problem of forecasting the dynamics of express delivering quality elements classification, the paper proposed a novel method integrating Kano model with GM(1,1) model which is effective to perform fitting prediction. An approach of fuzzy questionnaires to modify Kano questionnaires which considered as subjective is suggested, and the GM (1,1) model is used to estimate and predict data according to the quality classification of fuzzy Kano model. An empirical study in express industry is provided to testify its feasibility and validity.

Reliability optimization for FMS (flexible manufacturing system) is very important. Traditional optimal algorithms applying to FMS reliability have many shortcomings. In this article, the authors take into account the characteristics of FMS and bring forward modular neural networks (NN) model based on genetic algorithm (GA) to optimize FMS reliability. Modular NN based on GA has good convergence and short learning time. The example shows that the new algorithm is feasible and effective.

The evaluation of credit guarantee risk (CGR) involves many complex problems such as how to quantify risk, how to express causal relationships of risk factors, how to fuse all kinds of information and evaluate risk with uncertainties. Thus the evaluation of CGR is very difficult because of its uncertainty and diverse information. Bayesian belief networks (BBN) are basically a framework for reasoning uncertainty and diverse information. This paper presents BBN as a new methodology to evaluate CGR. An example is presented by using the BBN method.