A multi-objective Monte Carlo tree search for forest harvest scheduling - Neto, Teresa; Constantino, Miguel; Martins, Isabel; Pedroso, João Pedro (European journal of operational research, 2020)
•A multi-objective harvest scheduling problem with wildlife goals is studied.•Monte Carlo tree search, an uncommon method in forestry management, is proposed.•Instances at a scale and a level other than small and pilot-study are used.•The method provides several efficient alternative solutions in a reasonable time.•More wildlife is not always obtained at the expense of much less profit. While the objectives of forest management vary widely and include the protection of resources in protected forests and nature reserves, the primary objective has often been the production of wood products. However, even in this case, forests play a key role in the conservation of living resources. Constraining the areas of clearcuts contributes to this conservation, but if it is too restrictive, a dispersion of small clearcuts across the forest might occur, and forest fragmentation might be a serious ecological problem. Forest fragmentation leads to habitat loss, not only because the forest area is reduced, but also because the core area of the habitats and the connectivity between them decreases. This study presents a Monte Carlo tree search method to solve a bi-objective harvest scheduling problem with constraints on the clearcut area, total habitat area and total core area inside habitats. The two objectives are the maximization of both the net present value and the probability of connectivity index. The method is presented as an approach to assist the decision maker in estimating efficient alternative solutions and the corresponding trade-offs. This approach was tested with instances for forests ranging from some dozens to over a thousand stands and temporal horizons from three to eight periods. In general, multi-objective Monte Carlo tree search was able to find several efficient alternative solutions in a reasonable time, even for medium and large instances.