Add a proportion decision to a conservation planning problem. This is a relaxed decision where a part of a planning unit can be prioritized as opposed to the entire planning unit. Typically, this decision has the assumed action of buying a fraction of a planning unit to include in decisions will solve much faster than problems that use binary-type decisions

add_proportion_decisions(x)

Arguments

x

ConservationProblem-class object.

Value

ConservationProblem-class object with the decisions added to it.

Details

Conservation planning problems involve making decisions on planning units. These decisions are then associated with actions (e.g. turning a planning unit into a protected area). If no decision is explicitly added to a problem, then the binary decision class will be used by default. Only a single decision should be added to a ConservationProblem object. If multiple decisions are added to a problem object, then the last one to be added will be used.

See also

Examples

# set seed for reproducibility set.seed(500) # load data data(sim_pu_raster, sim_features, sim_pu_zones_stack, sim_features_zones) # create minimal problem with proportion decisions p1 <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_proportion_decisions()
# solve problem s1 <- solve(p1)
#> Optimize a model with 5 rows, 90 columns and 450 nonzeros #> Coefficient statistics: #> Matrix range [2e-01, 9e-01] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [3e+00, 8e+00] #> Presolve time: 0.00s #> Presolved: 5 rows, 90 columns, 450 nonzeros #> #> Iteration Objective Primal Inf. Dual Inf. Time #> 0 0.0000000e+00 3.170407e+01 0.000000e+00 0s #> 12 1.9315819e+03 0.000000e+00 0.000000e+00 0s #> #> Solved in 12 iterations and 0.00 seconds #> Optimal objective 1.931581909e+03
# plot solutions plot(s1, main = "solution")
# build multi-zone conservation problem with proportion decisions p2 <- problem(sim_pu_zones_stack, sim_features_zones) %>% add_min_set_objective() %>% add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5, ncol = 3)) %>% add_proportion_decisions()
# solve the problem s2 <- solve(p2)
#> Optimize a model with 105 rows, 270 columns and 1620 nonzeros #> Coefficient statistics: #> Matrix range [2e-01, 1e+00] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [1e+00, 2e+01] #> Presolve removed 3 rows and 0 columns #> Presolve time: 0.00s #> Presolved: 102 rows, 270 columns, 1350 nonzeros #> #> Iteration Objective Primal Inf. Dual Inf. Time #> 0 0.0000000e+00 1.319305e+02 0.000000e+00 0s #> 94 1.0964841e+04 0.000000e+00 0.000000e+00 0s #> #> Solved in 94 iterations and 0.00 seconds #> Optimal objective 1.096484061e+04
# print solution print(s2)
#> class : RasterStack #> dimensions : 10, 10, 100, 3 (nrow, ncol, ncell, nlayers) #> resolution : 0.1, 0.1 (x, y) #> extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax) #> coord. ref. : NA #> names : layer.1.1, layer.1.2, layer.1.3 #> min values : 0, 0, 0 #> max values : 1, 1, 1 #>
# plot solution # panels show the proportion of each planning unit allocated to each zone plot(s2, axes = FALSE, box = FALSE)