Optimization with Expected Values of Random Yield Coefficients in Renewable Resource Linear Programs
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.
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Document Type: Journal Article
Affiliations: Professor, Department of Forest and Wood Sciences, Colorado State University
Publication date: 1988-09-01
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