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Using integer programming techniques for the solution of an experimental design problem

  • Discrete Optimization
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Abstract

Latin hypercube sampling is often used to estimate the distribution function of a complicated function of many random variables. In so doing, it is typically necessary to choose a permutation matrix which minimizes the correlation among the cells in the hypercube layout. This problem can be formulated as a generalized, multi-dimensional assignment problem. For the two-dimensional case, we provide a polynomial algorithm. For higher dimensions, we offer effective heuristic and bounding procedures.

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Authors and Affiliations

  1. Department of Operations Research and Engineering, George Mason University, 22030, Fairfax, VA, USA

    Carl M. Harris & Karla L. Hoffman

  2. Chesapeake Decision Sciences, Inc., 07974, New Providence, NJ, USA

    Leslie-Ann Yarrow

Authors
  1. Carl M. Harris
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  2. Karla L. Hoffman
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  3. Leslie-Ann Yarrow
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Additional information

Supported in part by a grant from the National Institute of Standards and Technology (60NANB9D-0974).

Supported in part by grants from the Office of Naval Research (N00014-90-J-1324) and the Air Force Office of Scientific Research (F49 620-90-C-0022).

Research partially performed while visiting the Department of Mathematics, Brunel University, Uxbridge, England.

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Harris, C.M., Hoffman, K.L. & Yarrow, LA. Using integer programming techniques for the solution of an experimental design problem. Ann Oper Res 58, 243–260 (1995). https://doi.org/10.1007/BF02032134

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  • DOI: https://doi.org/10.1007/BF02032134

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