Monte Carlo method can analyze, solve and optimize many mathematical or physical problems through generating a large number of statistical random samples to simulating stochastic events. It also can be used to remarkably improve design quality of new product. In new product design process, setting distribution characteristics of the design variables is vital to product quality and production robustness. Firstly, response surface model between output characteristics and design variables in new product design is proposed, and the distribution characteristics of design variables and response output are analyzed; then position error model of response output and standard value and allowed error maximum is presented; and then the differences of position error model and allowed error maximum are counted, and reliability ratio is built and calculated, and design robustness of the new product is increased by adjusting the precision value of random design variables in Monte Carlo experiments. Finally, a case is brought forward to verify the validity of the method.
 Fonseca, J.R., Friswell, M.I. and Lees, A.W. (2007) Efficient Robust Design via Monte Carlo Sample Reweighting. International Journal for Numerical Methods in Engineering, 69, 2279-2301.
 Ray, L.R. and Stengel, R.F. (1993)A Monte Carlo Approach to the Analysis of Control System Robustness. Automatica, 29, 229-236. http://dx.doi.org/10.1016/0005-1098(93)90187-X
 Arnér, M. (2014) Monte Carlo Methods for Robust Design. Statistical Robust Design: An Industrial Perspective. Published Online, 177-193. http://dx.doi.org/10.1002/9781118842003.ch10
 Conceicao, E.L.T. and Portugal, A.A.T.G. (2011) Finite-Sample Comparison of Robust Estimators for Nonlinear Regression Using Monte Carlo Simulation: Part I. Univariate Response Models. Computers & Chemical Engineering, 35, 530-544. http://dx.doi.org/10.1016/j.compchemeng.2010.04.009
 Todorov, V., Neykov, N. and Neytchev, P. (1994) Robust Two-Group Discrimination by Bounded Influence Regression: A Monte Carlo Simulation. Computational Statistics & Data Analysis, 17, 289-302. http://dx.doi.org/10.1016/0167-9473(94)90122-8
 Mandur, J. and Budman, H. (2014) Robust Optimization of Chemical Processes Using Bayesian Description of Parametric Uncertainty. Journal of Process Control, 24, 422-430.
 Wang, Y., Cao, Z.J. and Au, S.-K. (2010) Efficient Monte Carlo Simulation of Parameter Sensitivity in Probabilistic Slope Stability Analysis. Computers and Geotechnics, 37, 1015-1022.
 Meng, X.-J., Zhang, C., Zhan, M.-J. and Shi, Z.-X. (2004) Robustness Analysis of Locus Generating Linkage through Monte-Carlo Method. Journal of Mechanical Science and Technology, 23, 203, 205, 214.
 Zhang, L., Zhang, W.M. and Shen, Y.H. (2006) Robust Design for Locus Generating Steering Mechanism Based on Monte Carlo Method. Journal of University of Science and Technology Beijing, 28, 1174-1177.
 Yan, Y.D., Meng, Q., Wang, S.A. and Guo, X.C. (2012) Robust Optimization Model of Schedule Design for a Fixed Bus Route. Transportation Research Part C: Emerging Technologies, 25, 113-121.
 Varas, M., Maturana, S. and Pascual, R. (2014) Ignacio Vargas, Jorge Vera. Scheduling Production for a Sawmill: A Robust Optimization Approach. International Journal of Production Economics, 150, 37-51. http://dx.doi.org/10.1016/j.ijpe.2013.11.028
 Boylan, G.L., Goethals, P.L. and Cho, B.R. (2013) Robust Parameter Design in Resource-Constrained Environments: An Investigation of Trade-Offs between Costs and Precision within Variable Processes. Applied Mathematical Modelling, 37, 2394-2416.