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.
References
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[2]
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[3]
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http://dx.doi.org/10.1016/j.compgeo.2010.08.010
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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.
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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.
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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.
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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.
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[13]
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. http://dx.doi.org/10.1016/j.apm.2012.05.017
