Add mandatory allocation constraints

Add constraints to a conservation planning problem to ensure that every planning unit is allocated to a management zone in the solution. Note that this function can only be used with problems that contain multiple zones.

Usage

add_mandatory_allocation_constraints(x)

Arguments

x

problem() object.

Value

An updated problem() object with the constraints added to it.

Details

For a conservation planning problem() with multiple management zones, it may sometimes be desirable to obtain a solution that assigns each and every planning unit to a zone. For example, when developing land-use plans, some decision makers may require that every parcel of land is allocated a specific land-use type. In other words are no "left over" areas. Although it might seem tempting to simply solve the problem and manually assign "left over" planning units to a default zone afterwards (e.g., an "other", "urban", or "grazing" land-use), this could result in highly sub-optimal solutions if there are penalties for siting the default land-use adjacent to other zones. Instead, this function can be used to specify that all planning units in a problem with multiple zones must be allocated to a management zone (i.e., zone allocation is mandatory).

See also

See constraints for an overview of all functions for adding constraints.

Other constraints: add_contiguity_constraints(), add_feature_contiguity_constraints(), add_linear_constraints(), add_locked_in_constraints(), add_locked_out_constraints(), add_manual_bounded_constraints(), add_manual_locked_constraints(), add_neighbor_constraints()

Examples

# \dontrun{
# set seed for reproducibility
set.seed(500)

# load data
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()

# create multi-zone problem with minimum set objective
targets_matrix <- matrix(rpois(15, 1), nrow = 5, ncol = 3)

# create minimal problem with minimum set objective
p1 <-
  problem(sim_zones_pu_raster, sim_zones_features) %>%
  add_min_set_objective() %>%
  add_absolute_targets(targets_matrix) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# create another problem that is the same as p1, but has constraints
# to mandate that every planning unit in the solution is assigned to
# zone
p2 <- p1 %>% add_mandatory_allocation_constraints()

# solve problems
s1 <- solve(p1)
s2 <- solve(p2)

# convert solutions into category layers, where each pixel is assigned
 # value indicating which zone it was assigned to in the zone
c1 <- category_layer(s1)
c2 <- category_layer(s2)

# plot solution category layers
plot(c(c1, c2), main = c("default", "mandatory allocation"), axes = FALSE)

# }