Add robust constraints

Add constraints to a conservation planning problem to explicitly account for uncertainty.

Details

The robust constraints functions are designed to be used with a robust objective function (e.g., add_robust_min_set_objective(), add_robust_min_shortfall_objective()). In particular, these functions are used to specify which features should be grouped together for characterizing plausible realizations. For example, if considering considering multiple projections for a species' distribution, these constraints are used to specify which features correspond to the same species. In addition to specifying feature groups, these constraints are also used to specify a confidence level that describes the level of robustness required for solutions.

The following robust constraint functions can be added to a conservation planning problem:

add_constant_robust_constraints(): Add robust constraints to a conservation problem to specify that the solution should ideally aim for the same level of robustness for each feature group.
add_variable_robust_constraints(): Add robust constraints to a conservation problem to specify that the solution should ideally aim for different levels of robustness for each feature group.

Examples

if (FALSE) { # \dontrun{
# Load packages
library(prioritizr)
library(terra)
library(tibble)

# Get planning unit data
pu <- get_sim_pu_raster()

# Get feature data
features <- get_sim_features()

# Define the feature group data
# Here, we will assign the first 2 features to the group A, and the
# remaining features to the group B
groups <- c(rep("A", 2), rep("B", nlyr(features) - 2))

# Build problem with constant robust constraints
p1 <-
  problem(pu, features) |>
  add_constant_robust_constraints(groups = groups, conf_level = 0.5) |>
  add_robust_min_set_objective() |>
  add_relative_targets(0.1) |>
  add_binary_decisions() |>
  add_default_solver(verbose = FALSE)

# Next, we will create the input data for adding variable robust
# constraints. In particular, we specify a confidence level of 0.95 for
# group A, and a confidence level of 0.5 for group B
constraint_data <- tibble(
  features = split(names(features), groups),
  conf_level = c(0.95, 0.5)
)

# Display constraint data
print(constraint_data)

# Build problem with variable robust constraints
p2 <-
  problem(pu, features) |>
  add_variable_robust_constraints(data = constraint_data) |>
  add_robust_min_set_objective() |>
  add_relative_targets(0.1) |>
  add_binary_decisions() |>
  add_default_solver(verbose = FALSE)

# Solve the problems
soln <- c(solve(p1), solve(p2))
names(soln) <- c(
  "constant robust constraints", "variable robust constraints"
)

# Plot the solutions
plot(soln)
} # }

Details

See also

Examples