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Pragmatic Approaches to Optimization with Random Yield Coefficients

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This paper discusses practical methods for handling normally distributed random technical (yield) coefficients in linear programs that optimize natural resource allocation and scheduling. These methods are practical in the sense that they are applicable to large-scale real world models and do not require nonlinear solution methods. The paper begins with a description and demonstration of postoptimization approaches that are applicable to large, linear problems, and then explores methods for reducing overall risk through land allocation diversification. A central theme of the paper is the importance of providing some sort of allowance for uncertainty when presenting optimization results, which promotes a more realistic view of the problem by analysts and decision makers alike. For. Sci. 41(3):501-512.
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Keywords: Linear programming; chance constraints; probabilistic programming

Document Type: Journal Article

Affiliations: Professor, Michigan Technological University, Houghton, MI

Publication date: 1995-08-01

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

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    June 1, 2016 to Feb. 28, 2017

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