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The Effects of Neighborhood Storage Size on Dynamic Programming Solutions

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Forward recursive dynamic programming (DP) was used to investigate the sensitivity of two- and three-state dynamic programming networks to neighborhood storage. Two-state DP networks were formed using all pairwise combinations of four state variables (basal area, cubic foot volume, number of trees, and diameter) and various neighborhood intervals for each state variable. As state neighborhoods decreased in size, all solutions were converged on a single rotation scheme and objective function value. Large state neighborhoods with nonuniform future growth and value potentials result in suboptimal solutions and incorrect sensitivity analyses. Comparison of various two-state networks indicate that models incorporating net cubic volume are less sensitive to increases in state neighborhood size. The addition of both a forward "look ahead" heuristic and the "harvest type" state variable to two-state networks improved the objective function value when neighborhoods were large and had nonuniform intra-neighborhood future growth and value potentials. However, these additional efforts resulted in no improvement in the objective function value when small state neighborhoods were used, further supporting the hypothesis that the DP algorithm did find the global optimum solution for the given problem formulation. For. Sci. 43(3):387-395.

Keywords: Optimization; aggregation; numerical methods

Document Type: Journal Article

Affiliations: Associate Professor, University of Kentucky, Department of Forestry, Lexington, KY 40546-0073:, Fax: (606)323-1031

Publication date: 1997-08-01

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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.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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