A learning heuristic for integrating spatial and temporal detail in forest planning. (21st February 2021)

• Record Type: Journal Article
• Authors: Henderson, Eric B.; Hoganson, Howard M.
• Is Part Of: Natural resource modelling. Volume 34:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-21
• Subjects: core area -- decomposition -- dynamic programming -- forest management -- harvest scheduling -- Kirtland's Warbler -- meta‐heuristics -- optimization; Conservation of natural resources -- Mathematical models -- Periodicals; Ecology -- Mathematical models -- Periodicals; 371.397
• DOI: 10.1111/nrm.12299
• Languages: English
• ISSNs: 0890-8575

Abstract

Abstract: We present a learning heuristic using dynamic programming (DP) formulations to address both spatial and temporal detail in multiobjective forest management planning. The problem is decomposed into smaller problems to avoid the curse of dimensionality associated with DP. The heuristic learns from multiple decomposed problem formulations to identify stands assigned the same management option regardless of formulation. Consistently managed stands are recognized, and the problem is eventually distilled to the most difficult to solve portions of the forest. The heuristic is demonstrated on a forest management problem with short‐lived core area wildlife habitat, where temporal detail is important. The problem is large due to the number of stand‐level management timing options and associated interactions with the management timings of nearby stands. Results show solution improvement along with substantial time savings over previously used heuristics. The learning heuristic enables analysis of large problems that emerge with high levels of spatial and temporal detail. Recommendations for Resource Managers: Adding spatial and temporal detail to forest planning models can improve the utility of the solution for some applications such as wildlife habitat management. Decomposing a large optimization problem into linked, smaller subproblems can speed the solution search time. Learning from problem decomposition solutions to formulate subsequent search attempts can improve the quality of a solution.