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The Deterministic Equivalents of Chance-Constrained Programming

Author

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  • Kim, C.S.
  • Schaible, Glenn D.
  • Segarra, Eduardo

Abstract

Three concepts combine to show both the feasibility and desirability of incorporating probability within programming models. First, the reliability of estimates obtained by using Chebyshev's inequality increases as variation measured by the coefficient of variation, declines. Second, the coefficient of variation can be substantially reduced by the use of the mean and variance of a truncated normal distribution. Third, chance-constrained programming can be converted into deterministic equivalent quadratic programming by using the parameters of a truncated normal distribution.

Suggested Citation

  • Kim, C.S. & Schaible, Glenn D. & Segarra, Eduardo, 1990. "The Deterministic Equivalents of Chance-Constrained Programming," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, vol. 42(2), pages 1-9.
    • Handle: RePEc:ags:uersja:139022
    DOI: 10.22004/ag.econ.139022
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    References listed on IDEAS

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    1. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    2. John T. Scott & Chester B. Baker, 1972. "A Practical Way to Select an Optimum Farm Plan Under Risk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 54(4_Part_1), pages 657-660.
    3. Joyce T. Chen, 1973. "Quadratic Programming for Least-Cost Feed Formulations under Probabilistic Protein Constraints," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 55(2), pages 175-183.
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    Cited by:

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    2. Giada Spaccapanico Proietti & Mariagiulia Matteucci & Stefania Mignani & Bernard P. Veldkamp, 2024. "Chance-Constrained Automated Test Assembly," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 92-120, February.
    3. Athanasios Kampas & Ben White, 2004. "Administrative Costs and Instrument Choice for Stochastic Non-point Source Pollutants," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 27(2), pages 109-133, February.

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