A process simulation based method for scheduling product design change propagation

  • Authors:

  • Yuliang Li;Wei Zhao;Xinyu Shao

  • Affiliations:

  • The State Key Lab of CAD&CG, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province 310027, China;Department of Foreign Language, Zhejiang University of Finance and Economics, 18 Xueyuan Street, Hangzhou, Zhejiang Province 310018, China;School of Mechanical Engineering and Science, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan, Hubei Province 430074, China

  • Venue:
  • Advanced Engineering Informatics
  • Year:
  • 2012

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Abstract

It is expected by enterprises that design changes that occur in a complex product development process can be resolved in a cost-effective way so that products modified for new customer requirements or new technology can be launched into the market without increasing much cost and time. Usually there are different solutions or methods for design changes, which may have different impacts on the whole process. Since a complex product development process consists of many design tasks and different relationships among these tasks, it is not easy to analytically compute the completion time when change propagation finishes. In this paper a process simulation based method is proposed to select the most economic propagation path for each design change, which can reduce the total process time for changes occurring in the complex product development process. ''And/Or'' graph nodes are used to represent the input and output logics of each task in the design process. Mathematic models are developed to calculate the completion time of the change propagation process. Monte Carlo based simulation algorithms for finding feasible output paths and calculating impacts of input changes on tasks are developed in order to simulate the change propagation process. The objective function for scheduling design change propagations is obtained by the regression analysis of simulation results. And the best change propagation paths, which usually are not the real optimum of the original design process model, are given by optimizing the regression model with a commercial optimization package. The effect of the method is demonstrated with a study of change propagations in the motor cycle engine design process.