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Publicação
A progressive hedging approach to solve harvest scheduling problem under climate change
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Orientador(es)
Resumo(s)
Due to the long time horizon typically characterizing forest planning, uncertainty plays an
important role when developing forest management plans. Especially important is the uncertainty
related to recently human-induced global warming since it has a clear impact on forest capacity
to contribute to biogenic and anthropogenic ecosystem services. If the forest manager ignores
uncertainty, the resulting forest management plan may be sub-optimal, in the best case. This paper
presents a methodology to incorporate uncertainty due to climate change into forest management
planning. Specifically, this paper addresses the problem of harvest planning, i.e., defining which
stands are to be cut in each planning period in order to maximize expected net revenues, considering
several climate change scenarios. This study develops a solution approach for a planning problem for
a eucalyptus forest with 1000 stands located in central Portugal where expected future conditions are
anticipated by considering a set of climate scenarios. The model including all the constraints that
link all the scenarios and spatial adjacency constraints leads to a very large problem that can only be
solved by decomposing it into scenarios. For this purpose, we solve the problem using Progressive
Hedging (PH) algorithm, which decomposes the problem into scenario sub-problems easier to solve.
To analyze the performance of PH versus the use of the extensive form (EF), we solve several instances
of the original problem using both approaches. Results show that PH outperforms the EF in both
solving time and final optimality gap. In addition, the use of PH allows to solve the most di cult
problems while the commercial solvers are not able to solve the EF. The approach presented allows
the planner to develop more robust management plans that incorporate the uncertainty due to climate
change in their plans
Descrição
Palavras-chave
stochastic programming progressive hedging harvest scheduling adjacency constraints
Contexto Educativo
Citação
Forests 2020, 11, 224
Editora
MDPI