add_min_shortfall_objective {prioritizr}

Add minimum shortfall objective

Description

Set the objective of a conservation planning problem to minimize the overall shortfall for as many targets as possible while ensuring that the cost of the solution does not exceed a budget.

Usage

add_min_shortfall_objective(x, budget)

Arguments

x

problem() object.

budget

numeric value specifying the maximum expenditure of the prioritization. For problems with multiple zones, the argument to budget can be (i) a single numeric value to specify a single budget for the entire solution or (ii) a numeric vector to specify a separate budget for each management zone.

Details

The minimum shortfall objective aims to find the set of planning units that minimize the overall (weighted sum) shortfall for the representation targets—that is, the fraction of each target that remains unmet—for as many features as possible while staying within a fixed budget (inspired by Table 1, equation IV, Arponen et al. 2005). Additionally, weights can be used to favor the representation of certain features over other features (see add_feature_weights().

Value

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

Mathematical formulation

This objective can be expressed mathematically for a set of planning units (I indexed by i) and a set of features (J indexed by j) as:

\mathit{Minimize} \space \sum_{j = 1}^{J} w_j \frac{y_j}{t_j} \\ \mathit{subject \space to} \\ \sum_{i = 1}^{I} x_i r_{ij} + y_j \geq t_j \forall j \in J \\ \sum_{i = 1}^{I} x_i c_i \leq B

Here, x_i is the decisions variable (e.g., specifying whether planning unit i has been selected (1) or not (0)), r_{ij} is the amount of feature j in planning unit i, t_j is the representation target for feature j, y_j denotes the representation shortfall for the target t_j for feature j, and w_j is the weight for feature j (defaults to 1 for all features; see add_feature_weights() to specify weights). Additionally, B is the budget allocated for the solution, c_i is the cost of planning unit i. Note that y_j is a continuous variable bounded between zero and infinity, and denotes the shortfall for target j.

References

Arponen A, Heikkinen RK, Thomas CD, and Moilanen A (2005) The value of biodiversity in reserve selection: representation, species weighting, and benefit functions. Conservation Biology, 19: 2009–2014.

See Also

See objectives for an overview of all functions for adding objectives. Also, see targets for an overview of all functions for adding targets, and add_feature_weights() to specify weights for different features.

Other objectives: add_max_cover_objective(), add_max_features_objective(), add_max_phylo_div_objective(), add_max_phylo_end_objective(), add_max_utility_objective(), add_min_largest_shortfall_objective(), add_min_set_objective()

Examples

## Not run: 
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()

# create problem with minimum shortfall objective
p1 <-
  problem(sim_pu_raster, sim_features) %>%
  add_min_shortfall_objective(1800) %>%
  add_relative_targets(0.1) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s1 <- solve(p1)

# plot solution
plot(s1, main = "solution", axes = FALSE)

# create multi-zone problem with minimum shortfall objective,
# with 10% representation targets for each feature, and set
# a budget such that the total maximum expenditure in all zones
# cannot exceed 3000
p2 <-
  problem(sim_zones_pu_raster, sim_zones_features) %>%
  add_min_shortfall_objective(3000) %>%
  add_relative_targets(matrix(0.1, ncol = 3, nrow = 5)) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s2 <- solve(p2)

# plot solution
plot(category_layer(s2), main = "solution", axes = FALSE)

# create multi-zone problem with minimum shortfall objective,
# with 10% representation targets for each feature, and set
# separate budgets for each management zone
p3 <-
  problem(sim_zones_pu_raster, sim_zones_features) %>%
  add_min_shortfall_objective(c(3000, 3000, 3000)) %>%
  add_relative_targets(matrix(0.1, ncol = 3, nrow = 5)) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s3 <- solve(p3)

# plot solution
plot(category_layer(s3), main = "solution", axes = FALSE)

## End(Not run)