Conservation planning problems involve making decisions on
how different planning units will be managed. These decisions might involve
turning an entire planning unit into a protected area, turning part
of a planning unit into a protected area, or allocating a planning
unit to a specific management zone. If no decision is explicitly added to a
`problem`

, then binary decisions will be used by default.

Only a single type of decision can be added to a conservation
planning `problem`

.**If multiple decisions are added
to problem, then the last one to be added will be used.**

The following decisions can be added to a conservation planning
`problem`

:

`add_binary_decisions`

Add a binary decision to a conservation planning

`problem`

. This is the classic decision of either prioritizing or not prioritizing a planning unit. Typically, this decision has the assumed action of buying the planning unit to include in a protected area network. If no decision is added to a problem object then this decision class will be used by default.`add_proportion_decisions`

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 default of the entire planning unit. Typically, this decision has the assumed action of buying a fraction of a planning unit to include in a protected area network.`add_semicontinuous_decisions`

Add a semi-continuous decision to a conservation planning

`problem`

. This decision is similar to`add_proportion_decision`

except that it has an upper bound parameter. By default, the decision can range from prioritizing none (0 %) to all (100 %) of a planning unit. However, a upper bound can be specified to ensure that at most only a fraction (e.g. 80 %) of a planning unit can be preserved. This type of decision may be useful when it is not practical to conserve the entire area encompassed by any single planning unit.

# load data data(sim_pu_raster, sim_features) # create basic problem and using the default decision types (binary) p1 <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) # create problem with manually specified binary decisions p2 <- p1 %>% add_binary_decisions() # create problem with proportion decisions p3 <- p1 %>% add_proportion_decisions() # create problem with semicontinuous decisions p4 <- p1 %>% add_semicontinuous_decisions(upper_limit = 0.5)#> Optimize a model with 5 rows, 90 columns and 450 nonzeros #> Variable types: 0 continuous, 90 integer (90 binary) #> 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] #> Found heuristic solution: objective 2337.9617505 #> Presolve time: 0.00s #> Presolved: 5 rows, 90 columns, 450 nonzeros #> Variable types: 0 continuous, 90 integer (90 binary) #> Presolved: 5 rows, 90 columns, 450 nonzeros #> #> #> Root relaxation: objective 1.931582e+03, 12 iterations, 0.00 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 1931.58191 0 4 2337.96175 1931.58191 17.4% - 0s #> H 0 0 1985.6818841 1931.58191 2.72% - 0s #> #> Explored 1 nodes (12 simplex iterations) in 0.00 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 2: 1985.68 2337.96 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 1.985681884076e+03, best bound 1.931581908865e+03, gap 2.7245% #> Optimize a model with 5 rows, 90 columns and 450 nonzeros #> Variable types: 0 continuous, 90 integer (90 binary) #> 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] #> Found heuristic solution: objective 2337.9617505 #> Presolve time: 0.00s #> Presolved: 5 rows, 90 columns, 450 nonzeros #> Variable types: 0 continuous, 90 integer (90 binary) #> Presolved: 5 rows, 90 columns, 450 nonzeros #> #> #> Root relaxation: objective 1.931582e+03, 12 iterations, 0.00 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 1931.58191 0 4 2337.96175 1931.58191 17.4% - 0s #> H 0 0 1985.6818841 1931.58191 2.72% - 0s #> #> Explored 1 nodes (12 simplex iterations) in 0.00 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 2: 1985.68 2337.96 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 1.985681884076e+03, best bound 1.931581908865e+03, gap 2.7245% #> 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 #> 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 [5e-01, 5e-01] #> 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 1.9495280e+03 0.000000e+00 0.000000e+00 0s #> #> Solved in 12 iterations and 0.00 seconds #> Optimal objective 1.949528006e+03# plot solutions plot(s, main = c("default (binary)", "binary", "proportion", "semicontinuous (upper = 0.5)"))