Set an objective to minimize the number of planning units

Specify that a restoration problem (restopt_problem()) should minimize the number of planning units.

Usage

set_min_nb_pus_objective(problem)

Arguments

problem

restopt_problem() Restoration problem object.

Value

An updated restoration problem (restopt_problem()) object.

Details

Planning units correspond to aggregated cells from the original dataset. Minimizing the number of planning units reduces the spatial extent of the restoration area.

See also

Other objectives: set_max_iic_objective(), set_max_mesh_objective(), set_max_nb_pus_objective(), set_max_restore_objective(), set_min_restore_objective(), set_no_objective()

Examples

# \donttest{
# load data
habitat_data <- rast(
  system.file("extdata", "habitat_hi_res.tif", package = "restoptr")
)

locked_out_data <- rast(
 system.file("extdata", "locked_out.tif", package = "restoptr")
)

# plot data
plot(rast(list(habitat_data, locked_out_data)), nc = 2)


# create problem with locked out constraints
p <- restopt_problem(
    existing_habitat = habitat_data,
    aggregation_factor = 16,
    habitat_threshold = 0.7
  ) %>%
  set_min_nb_pus_objective() %>%
  add_restorable_constraint(
    min_restore = 5,
    max_restore = 5,
  ) %>%
  add_locked_out_constraint(data = locked_out_data) %>%
  add_settings(time_limit = 1)

# print problem
print(p)
#> ----------------------------------------------------------------- 
#>                          Restopt                          
#> ----------------------------------------------------------------- 
#> original habitat:     habitat_hi_res.tif 
#> aggregation factor:   16 
#> habitat threshold:    0.7 
#> existing habitat:     in memory 
#> restorable habitat:   in memory 
#> ----------------------------------------------------------------- 
#> objective:            Minimize number of planning units 
#> ----------------------------------------------------------------- 
#> constraints:          
#>   -  restorable (min_restore = 5, max_restore = 5, min_proportion = 1, unit = ha) 
#>   -  locked out (data = in memory) 
#> ----------------------------------------------------------------- 
#> settings: 
#>   - precision = 4
#>   - time_limit = 1
#>   - nb_solutions = 1
#>   - optimality_gap = 0
#>   - solution_name_prefix = Solution  
#> ----------------------------------------------------------------- 

# solve problem
s <- solve(p)
#> Good news: the solver found 1 solution statisfying the constraints that was proven optimal ! (solving time = 0.02 s)

# plot solution
plot(s)

# }