`R/add_max_features_objective.R`

`add_max_features_objective.Rd`

Set the objective of a conservation planning `problem()`

to
fulfill as many targets as possible while ensuring that the cost of the
solution does not exceed a budget.

`add_max_features_objective(x, budget)`

- x
`problem()`

(i.e.,`ConservationProblem`

) 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.

Object (i.e., `ConservationProblem`

) with the objective
added to it.

The maximum feature representation objective is an enhanced version of the
maximum coverage objective `add_max_cover_objective()`

because
targets can be used to ensure that a certain amount of each feature is
required in order for them to be adequately represented (similar to the
minimum set objective (see `add_min_set_objective()`

). This
objective finds the set of planning units that meets representation targets
for as many features as possible while staying within a fixed budget
(inspired by Cabeza and Moilanen 2001). Additionally, weights can be used
`add_feature_weights()`

). If multiple solutions can meet the same
number of weighted targets while staying within budget, the cheapest
solution is returned.

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

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

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
\(j\), \(y_j\) indicates if the solution has meet
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 \(i\), and \(s\) is a scaling factor used
to shrink the costs so that the problem will return a cheapest solution
when there are multiple solutions that represent the same amount of all
features within the budget.

Cabeza M and Moilanen A (2001) Design of reserve networks and the
persistence of biodiversity. *Trends in Ecology & Evolution*,
16: 242--248.

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_phylo_div_objective()`

,
`add_max_phylo_end_objective()`

,
`add_max_utility_objective()`

,
`add_min_largest_shortfall_objective()`

,
`add_min_set_objective()`

,
`add_min_shortfall_objective()`

```
# \dontrun{
# load data
data(sim_pu_raster, sim_pu_zones_stack, sim_features, sim_features_zones)
# create problem with maximum features objective
p1 <- problem(sim_pu_raster, sim_features) %>%
add_max_features_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, box = FALSE)
# create multi-zone problem with maximum features 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_pu_zones_stack, sim_features_zones) %>%
add_max_features_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, box = FALSE)
# create multi-zone problem with maximum features objective,
# with 10% representation targets for each feature, and set
# separate budgets for each management zone
p3 <- problem(sim_pu_zones_stack, sim_features_zones) %>%
add_max_features_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, box = FALSE)
# }
```