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Optimization with Expected Values of Random Yield Coefficients in Renewable Resource Linear Programs

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This paper analyzes the problem of optimization when the yield coefficients are random variables with known (or acceptably approximated) means. An important distinction is demonstrated between the case where only land area constraints are included and where both area and yield constraints are included. The latter case is shown to be substantially more difficult both in problem formulation and solution. A test case that addresses the latter case empirically is presented. A bootstrap technique and Monte Carlo repetitions are used to simulate the expected value of the optimum. The test case indicates that current practice may closely emulate finding the expected value of the maximum objective function, but with uncertain feasibility. For. Sci. 34(3):634-646.

Keywords: Optimization; fallacy of averages; mathematical programming; risk and uncertainty

Document Type: Journal Article

Affiliations: Professor, Department of Forest and Wood Sciences, Colorado State University

Publication date: September 1, 1988

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  • 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.
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