Provides the sum of costs to actions and monitoring applied in a solution.
getCost(x)The cost value is calculated as the sum of all the individual costs of actions and monitoring carried out in each of the planning units. This can be expressed mathematically for a set of planning units \(I\) indexed by \(i\), and a set of threats \(K\) indexed by \(k\) as:
$$ actions = \sum_{i \in I}\sum_{k \in K_i} x_{ik} c_{ik} $$
$$ monitoring = \sum_{i \in I} x_{i \cdot} c^{'}_{i} $$ Where, \(x_{ik}\) is the decisions variable that specify whether an action has been selected to abate threat \(k\) in planning unit \(i\) (1) or not (0), \(c_{ik}\) is the action cost to abate threat \(k\) in planning unit \(i\) and \(c^{'}_{i}\) is the monitoring cost of planning unit \(i\). The cost of monitoring is applied to all planning units where some type of action has been selected (conservation action, to abate threats or connectivity).
Note that there is an action per threat, so it is assumed that the index of the threat coincides with the index of the action used to abate it.
# \donttest{
# set seed for reproducibility
set.seed(14)
## Load data
data(sim_pu_data, sim_features_data, sim_dist_features_data,
sim_threats_data, sim_dist_threats_data, sim_sensitivity_data,
sim_boundary_data)
## Create data instance
problem_data <- inputData(
pu = sim_pu_data, features = sim_features_data, dist_features = sim_dist_features_data,
threats = sim_threats_data, dist_threats = sim_dist_threats_data,
sensitivity = sim_sensitivity_data, boundary = sim_boundary_data
)
## Create optimization model
problem_model <- problem(x = problem_data)
#> Warning: The blm argument was set to 0, so the boundary data has no effect
#> Warning: Some blm_actions argument were set to 0, so the boundary data has no effect for these cases
## Solve the optimization model
s <- solve(a = problem_model, time_limit = 2, output_file = FALSE, cores = 2)
#> Rcplex: num variables=396 num constraints=284
## Get costs
getCost(s)
#> solution_name monitoring threat_1 threat_2
#> 1 sol 352 193 193
# }