Chance-Constrained and Chance-Maximizing Mathematical Programs in Renewable Resource Management
Abstract:This paper discusses a broad range of approaches to optimizing natural resource allocation in the situation where amounts of available input(s) and/or amounts of desired output(s) are random. The paper begins by reviewing, the classic approach to this class of problems, "chance-constrained programming," followed by a discussion of "joint probability chance-constrained programming," and a new alternative "total probability chance-constrained programming." The paper then develops three "chance-maximizing" counterparts. Specific formulations for executing these approaches in natural resource management problems are developed that explicitly include the cumulative probability density functions. A forestry case example demonstrates .solution procedures and shows that the different approaches can yield substantially different results. Results from different solution procedures are also compared. For. Sci. 37(1):308-325.
Document Type: Journal Article
Affiliations: Associate Professor, School of Forestry and Wood Products, Michigan Technological University, Houghton, MI
Publication date: 1991-03-01
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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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