Spatially explicit forest harvest scheduling with difference equations

Spatially explicit harvest scheduling models optimize the layout of harvest treatments to best meet management objectives such as revenue maximization subject to a variety of economic and environmental constraints. A few exceptions aside, the mixed-integer programming core of every exact model in th...

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Bibliographic Details
Published in:Annals of operations research 2015-09, Vol.232 (1), p.235-257
Main Authors: St. John, Rachel, Toth, Sandor F
Format: Article
Language:English
Subjects:
Age
Algebra
Analysis
Boolean
Business and Management
Combinatorics
Computation
Difference equations
Forest management
Forestry
Harvest
Harvesting
Integer programming
Linear programming
Management
Mathematical models
Maximization
Methods
Operations research
Operations Research/Decision Theory
Optimization
Revenues
Scheduling
Stands
Studies
Theory of Computation
Timber
Variables
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Summary:Spatially explicit harvest scheduling models optimize the layout of harvest treatments to best meet management objectives such as revenue maximization subject to a variety of economic and environmental constraints. A few exceptions aside, the mixed-integer programming core of every exact model in the literature requires one decision variable for every applicable prescription for a management unit. The only alternative to this “brute-force” method has been a network approach that tracks the management pathways of each unit over time via four sets of binary variables. Named after their linear programming-based aspatial predecessors, Models I and II, along with Model III, which has no spatial implementation, each of these models rely on static volume and revenue coefficients that must be calculated pre-optimization. We propose a fundamentally different approach that defines stand volumes and revenues as variables and uses difference equations and Boolean algebra to transition forest units from one planning period to the next. We show via three sets of computational experiments that the new model is a computationally promising alternative to Models I and II.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-012-1301-4