Focal-Point Aggregation Under Area Restrictions through Spatially Constrained Optimal Harvest Scheduling

Abstract Existing exact optimization techniques for harvest-scheduling problems with area restrictions are unable to solve explicit spatial issues such as the preservation of old growth forest along with its adjacent units without a priori numeration to generate feasible clusters. This paper builds on a previously developed model, MF-Model I, to frame the problem as a focal-point aggregation problem subject to maximum opening size requirements. We conducted computational experiments to demonstrate how the focal-point aggregation issue of old growth preservation can be solved. Although the computational experiments were limited, the number of focal points and the number of planning periods were shown to have a relatively large impact on computational performance for optimization. Computational performance was found to be more sensitive to minimum preservation size than maximum opening size. Finally, we showed that the proposed approach was able to handle a different explicit spatial issue for avoiding the existence of units smaller than the minimum threshold size along with the maximum opening size requirement for a cluster.