4.5 Article

Operational harvest planning under forest road maintenance uncertainty

Journal

FOREST POLICY AND ECONOMICS
Volume 131, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.forpol.2021.102562

Keywords

Harvest scheduling; Road network; Maintenance delay; Stochastic model; Integer linear programming; Simulation

Funding

  1. Brazilian government agency CAPES (Coordination for the Improvement of Higher Education Personnel) [001]

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The study focused on evaluating the impact of stochastic delays of forest road maintenance on forest harvesting through a stochastic programming model and simulations. It was found that delays significantly affected the harvesting plan and timber volume, highlighting the importance of effective road maintenance scheduling for better forest management practices.
We solve two related problems: i) the harvest scheduling problem and ii) the forest road maintenance scheduling problem. The main objective is to evaluate the effect of stochastic delays of forest road maintenance on forest harvesting. We built a control scenario to evaluate a deterministic model without road maintenance delay and measured the impacts of the delay through a stochastic programming model and simulations. The example used has 400 stands of planted Pinus sp. managed for pulp production. The considered road network is approximately 570 km. A deterministic programming model was formulated for the forest regulation problem, maximizing the number of harvested stands. A Monte Carlo simulation was applied to generate a random seed disturbance. In the tested instances, the number of stands that would have been harvested according to the deterministic schedule but were not harvested, due to delays in the maintenance of segments of the roads, varied from 1 to 400. The timber volume harvested over the planning horizon varied considerably, with periods in which the value was even zero. The stochastic model proposed can be useful to assist managers in decision making. In addition, the approach may help with road classification and reducing risks for better management practices.

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