Abstract
ABSTRACT The aim of this study was to develop a mathematical model for the spatial forest planning of harvest activity scheduling. Thirty-eight (38) blocks were formed from stands aged 13 to 17 years considering a total area limit set to 350 hectares. The model was built in Excel ® spreadsheet and solved by CPLEX® optimization software using the Branch and bound method for the linear programming of problems with integer variables. The results of optimization are presented in the form of a map of spatial distribution of the selected blocks. The total harvested area was 5,989 hectares and the volume generated was 2,956,913 m3 in a planning horizon of five years. Higher results compared with those of traditional planning and efficiency to optimize forest harvesting proved to be a viable alternative to the inclusion of space and operational issues associated with forest harvesting.
Highlights
A characteristic of spatial forest planning is the generation of problems with many constraints and integer decision variables
Linear Programming (LP) is one of the classic optimization techniques, and is one of the most widely used among the techniques developed by operational research
It is often used in forest management, especially in forest planning for timber harvest scheduling (Ezquerro et al, 2016)
Summary
A characteristic of spatial forest planning is the generation of problems with many constraints and integer decision variables. Linear Programming (LP) is one of the classic optimization techniques, and is one of the most widely used among the techniques developed by operational research It is often used in forest management, especially in forest planning for timber harvest scheduling (Ezquerro et al, 2016). For Weintraub & Murray (2006), at the tactical level, the decisions involve the planning period, during which each unit will be harvested in order to meet expected future demands, and demands a definition of access roads to harvesting units. This adds spatial characteristics to the planning process and requires linear programming models with binary variables
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