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. 2018 Oct 8;8:14964. doi: 10.1038/s41598-018-33389-9

Table 2.

List of the sets, variables and input data applied in the optimization models.

Set Description
S: set of stands in the area
M: set of management regimes
P: number of periods
C: block of stands that do not respect the minimum area limit
C: neighborhood set of the block C
CMA: set of all blocks of stands that do not respect the minimum area limit
Gi: set of center points within stand i
PT: set of all candidate center points for deadwood islands
Function Description
L(Φ(x)) Piecewise linearization of the function In(cosh(x))
Variable Description
xij: binary decision variable that takes value 1 case stand i is managed under regime j or 0 otherwise
auxz i integer auxiliary variable that equal the variable zi case variable xij takes value 1 and 0 otherwise
zi: integer variable that equal the number of deadwood islands allocated to stand i
yi: binary variable that takes value 1 case stand i is selected as forest reserve and value 0 otherwise
VolB: bound for the volume production at each period k
pti: binary variable that takes value 1 case center point i is selected as part of the solution and value 0 otherwise
sumPT: variable that expresses the total number of points in the deadwood island network
linSumij: auxiliary variable that linearizes the multiplication of the variable sumPT by variables kij
kij: binary variable that assumes value 1 case the arc connecting center points i and j is selected to be part of the solution and value 0 otherwise
flowij: flow travelling through arc (i,j)
Data Description
npvij: Net Present Value generated by stand i under management regime j
areai: area of stand i
Tarea: total forest area
|C|: number of stands in block C
BPT: bound on the number of center points in a single stand
AdjPT: adjacency matrix of the set of points PT
|PT|: number of points in PT
vijk: volume produced by stand i, under management j in period k
Enpv: Expected Net Present value
cholik: i-th row and k-th column element of the Cholesky decomposition of the covariance matrix