A Novel Flexible Experiment Design Method

  • Gang Zhai
  • Yaofei MaEmail author
  • Xiao Song
  • Yulin Wu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 603)


Latin Hypercube Design (LHD) is a traditional method of Design Of Experiments (DOE) and is often employed in system analysis. However, this method imposes restriction on experiment trials and needs much computation capacity to obtain the optimal design. A novel experiment design method called ETPLHD is proposed in this paper to solve this problem. ETPLHD can control the number of design points and thus presents more flexibility to control the number of experiment trails, which is more efficient compared to the fixed experiment trails in the traditional LHD method for a same design space. An experiment was conducted to compare ETPLHD with the other two experiment design algorithms. The results showed that TPLHD reveals high design performance and less time consumption.


Experiment design Simulation Latin Hypercube Design 


  1. 1.
    Sanchez, S.M., Wan, H.: Work smarter, not harder: a tutorial on designing and conducting simulation experiments. In: Proceedings of the 2012 Winter Simulation Conference, pp. 1–15 (2012)Google Scholar
  2. 2.
    Ghosh, S.P., Tuel, W.G.: A design of an experiment to model data base system performance. IEEE Trans. Softw. Eng. SE-2(2), 97–106 (1976)Google Scholar
  3. 3.
    McKay, M.D., Beckman, R.J.: A comparison of three methods for selecting values of input variables from a computer code. Technometrics 21(2), 239–245 (1979)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Kelton, W.D.: Designing simulation experiments. In: Proceedings of the 1999 Winter Conference, pp. 33–38 (1999)Google Scholar
  5. 5.
    Park, J.S.: Optimal Latin-hypercube designs for computer experiments. J. Stat. Plann. Inference 30(1), 95–111 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Morris, M.D., Mitchell, T.J.: Exploratory designs for computational experiments. J. Stat. Plann. Inference 43(3), 381–402 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Bates, S.J., Sienz, J., Toropov, V.V.: Formulation of the optimal Latin hypercube design of experiments using a permutation genetic algorithm. In: 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA-2004-2011, Palm Springs, California, pp. 19–22 (2004)Google Scholar
  8. 8.
    Viana, F.A.C., Venter, G., Balabanov, V.: An algorithm for fast optimal latin hypercube design of experiments. Int. J. Numerical Methods Eng. 82(2), 135–156 (2009)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Ye, K.Q., Li, W., Sudjianto, A.: Algorithmic construction of optimal symmetric latin hypercube designs. J. Stat. Plann. Inference 90, 145–159 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Ma, Y., Gong, G.: A research on CGF reasoning system based on Fuzzy Petri Net. In: Proceedings of System Simulation and Scientific Computing, Beijing, pp. 406–411 (2005)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.College of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina

Personalised recommendations