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Chance Constraints and Chance Maximization with Random Yield Coefficients in Renewable Resource Optimization

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This paper treats a variety of approaches to account for random yield coefficients with known means and variances in renewable resource optimization models. General formulations are discussed first, followed by a forestry case example that demonstrates the formulations and resulting optimal solutions in a renewable resource application. Different approaches to approximating the normal cumulative density function are evaluated using simulation. For. Sci. 38(2):305-323.
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Keywords: Linear programming; nonlinear programming; risk and uncertainty; stochastic models

Document Type: Journal Article

Affiliations: Associate Professor, School of Forestry and Wood Products, Michigan Technological University, Houghton, MI.

Publication date: 1992-04-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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