A Novel Cost Estimation Approach for Wood Harvesting Operations Using Symbolic Planning

Abstract

Abstract While forestry is an important economic factor, the methods commonly used to estimate potential financial gains from undertaking a harvesting operation are usually based on heuristics and experience. Those methods use an abstract view on the harvesting project at hand, focusing on a few general statistical parameters. To improve the accuracy of felling cost estimates, we propose a novel, single-tree-based cost estimation approach, which utilizes knowledge about the harvesting operation at hand to allow for a more specific and accurate estimate of felling costs. The approach utilizes well-known symbolic planning algorithms which are interfaced via the Planning Domain Definition Language (PDDL) and compile work orders. The work orders can then be used to estimate the total working time and thus the estimated cost for an individual harvesting project, as well as some additional efficiency statistics. Since a large proportion of today's harvesting operations are mechanized instead of motor manual, we focus on the planning of harvester and forwarder workflows. However, the use of these heavy forest machines carries the risk of damaging forest soil when repeatedly driving along skidding roads. Our approach readily allows for assessment of these risks.