Optimization of scale-based product family using multiobjective genetic algorithm

被引：0
|
作者：
State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou 310027, China [1 ]
机构：
[1] State Key Laboratory of CAD and CG, Zhejiang University
来源：
Zhejiang Daxue Xuebao (Gongxue Ban) | 2008年 / 6卷 / 1015-1020+1057期
关键词：
Multiobjective optimization; Nondominated sorting genetic algorithm II; Pareto set; Product platform; Scale-based product family;
D O I：
10.3785/j.issn.1008-973X.2008.06.023
中图分类号：
学科分类号：
摘要：
An optimization process using nondominated sorting genetic algorithm-II (NSGA-II) was put forward based on the theoretical model and the optimization model of scale-based product family. Based on the mathematical model of product family design, the Pareto set for the multiobjective optimization problem was obtained using NSGA-II. An approach based on fuzzy set theory was developed to extract one of the Pareto-optimal solutions as the best compromise one. During the first stage, each product in the family was optimized independently with NSGA-II. The design variables that showed small deviations were held constant to form the product platform. In the second stage, the scaling variables of each instance product were developed also using NSGA-II. The performance of instance product was improved on the premise that the design requirements of scale-based product family were satisfied. The efficiency and effectiveness of the proposed method were illustrated by the optimization design of the scale-based universal motor families and the comparison against the designs obtained from the one-stage-Ps method.
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页码：1015 / 1020+1057
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共 12 条
  • [1] Simpson T.W., Maier J.R.A., Mistree F., Product platform design: Method and application, Research in Engineering Design, 13, 1, pp. 2-22, (2001)
  • [2] Nayak R.U., Chen W., Simpson T.W., A variation-based method for product family design, Engineering Optimization, 34, 1, pp. 65-81, (2002)
  • [3] Dai Z.H., Scott M.J., Effective product family design using preference aggregation, Proceedings of DECT 2004 ASME Design Engineering Technical Conferences, pp. 57-419, (2004)
  • [4] Simpson T.W., Brayan D., Assessing variable levels of platform commonality within a product family using a multiobjective genetic algorithm, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, pp. 5-427, (2002)
  • [5] Thevenot H.J., Nanda J., Simpson T.W., A methodology to support product family redesign using a genetic algorithm and commonality indices, ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 84-927, (2005)
  • [6] Meyer M.H., Revitalize your product lines through continuous platform renewal, Research Technology Management, 40, 2, pp. 17-28, (1997)
  • [7] Srinivas N., Deb K., Multi-objective function optimization using nondominated sorting genetic algorithms, Evolutionary Computations, 2, 3, pp. 221-248, (1995)
  • [8] Deb K., Pratap A., Agarwal S., A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2, pp. 182-197, (2002)
  • [9] Sarkar D., Modak J.M., Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm, Chemical Engineering Science, 60, 2, pp. 481-492, (2005)
  • [10] Mitra K., Gopinath R., Multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm, Chemical Engineering Science, 59, 2, pp. 385-396, (2004)
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