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Forests 2017, 8(12), 476; https://doi.org/10.3390/f8120476

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

1
Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA
2
Department of Forestry and Wildland Resources, Humboldt State University, Arcata, CA 95521, USA
*
Author to whom correspondence should be addressed.
Received: 16 October 2017 / Revised: 15 November 2017 / Accepted: 1 December 2017 / Published: 3 December 2017
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Abstract

Adaptive forest management requires planning and implementation of activities designed to maintain or improve forest conditions, and in support of these endeavors knowledge of silviculture, economics, operations research, and other allied fields are necessary. With regard to forest planning, traditional (exact) mathematical techniques along with heuristics have been demonstrated as useful in developing alternative courses of action for forest managers to consider. In this discussion paper, we present six areas of future work with regard to investigations into the development of heuristics, along with several recommendations that are based on our experiences. These areas include process improvements, reversion strategies, destruction and reconstruction strategies, intelligent or dynamic parameterization approaches, intelligent termination or transitioning approaches, and seeding strategies. We chose the six areas based on our experiences in developing forest planning heuristics. These areas reflect our opinion of where future research might concentrate. All of these areas of work have the potential to enhance the capabilities and effectiveness of heuristic approaches when applied to adaptive forest management problems. View Full-Text
Keywords: forest planning; forest management; heuristic; combinatorial optimization forest planning; forest management; heuristic; combinatorial optimization
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Bettinger, P.; Boston, K. Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics. Forests 2017, 8, 476.

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