A Linear Programming-Probabilistic Approach to Decision Making under Uncertainty
Abstract:An approach, termed partially stochastic linear programming, combines linear programming with subjective probability estimates to quantitatively recognize uncertainty in forestry decision making. The approach is applied to minimizing wood procurement costs over an industrial firm's planning period. The future availabilities of land and raw material are considered random variables rather than point estimates. Subjective probability distributions on each random variable are developed from information obtained from the firm's forestry personnel. Alternative resource availability situations are simulated and least-cost linear programming solutions obtained. The results are a distribution of solutions which can be described by its mean and variance. The approach provides the manager with decision-making information, such as the probability of the cost being above or below a specified level, which is not available from deterministic linear programs. Limitations of the approach along with suggestions for application and additional study are discussed. Forest Sci. 17:224-229.
Document Type: Journal Article
Affiliations: Former Graduate Research Assistant, Division of Forestry and Wildlife Sciences, Virginia Polytechnic Institute and State University, Blacksburg
Publication date: 1971-06-01
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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.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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Journal of Forestry
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