Return one solution per instance for different values of budgets. This
function assumes that the *maximizeBenefits* option is being used (note that
the *minimizeCosts* option does not require setting a maximum budget). Like
`prioriactions()`

function, it inherits all arguments from `inputData()`

,
`problem()`

and `solve()`

.

`evalBudget(values = c(), ...)`

- values
`numeric`

. Values of budget to verify. More than one value is needed.- ...
arguments inherited from

`inputData()`

,`problem()`

, and`solve()`

functions.

An object of class portfolio.

`evalBudget()`

creates and solves multiple instances, of the corresponding
multi-actions planning problem, for different values of maximum budgets. Alternatively, this
could be obtained by executing function `prioriactions()`

or by steps the `inputData()`

,
`problem()`

and `solve()`

functions; using, in each run, different budgets values.
However, the `evalBudget()`

function has two advantages with
respect to this manual approach: : 1)
it is more efficient to create the models (this is because the model is created
just once and, at each iteration, only the budget values are updated); and 2) the
output is a portfolio object, which allows
obtaining information about the group of solutions (including all *get* functions).

```
# \donttest{
# set seed for reproducibility
set.seed(14)
## Create model and solve
port <- evalBudget(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,
values = c(1, 10, 50, 100),
time_limit = 50,
output_file = FALSE,
cores = 2)
#> *********************************
#> Iteration 1 of 4: Budget1
#> *********************************
#> 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
#> Rcplex: num variables=396 num constraints=277
#> *********************************
#> Iteration 2 of 4: Budget10
#> *********************************
#> 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
#> Rcplex: num variables=396 num constraints=277
#> *********************************
#> Iteration 3 of 4: Budget50
#> *********************************
#> 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
#> Rcplex: num variables=396 num constraints=277
#> *********************************
#> Iteration 4 of 4: Budget100
#> *********************************
#> 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
#> Rcplex: num variables=396 num constraints=277
getSolutionBenefit(port)
#> solution_name feature benefit.conservation benefit.recovery benefit.total
#> 1 Budget1 1 0 0 0
#> 2 Budget1 2 0 0 0
#> 3 Budget1 3 0 0 0
#> 4 Budget1 4 0 0 0
#> 5 Budget10 1 0 0 0
#> 6 Budget10 2 2 2 4
#> 7 Budget10 3 2 2 4
#> 8 Budget10 4 0 0 0
#> 9 Budget50 1 0 1 1
#> 10 Budget50 2 2 8 10
#> 11 Budget50 3 8 8 16
#> 12 Budget50 4 0 1 1
#> 13 Budget100 1 0 2 2
#> 14 Budget100 2 2 16 18
#> 15 Budget100 3 8 17 25
#> 16 Budget100 4 0 3 3
# }
```