Add manually specified locked constraints

Add constraints to a conservation planning problem() to ensure that solutions allocate (or do not allocate) specific planning units to specific management zones. This function offers more fine-grained control than the add_locked_in_constraints() and add_locked_out_constraints() functions.

add_manual_locked_constraints(x, data)

# S4 method for ConservationProblem,data.frame
add_manual_locked_constraints(x, data)

# S4 method for ConservationProblem,tbl_df
add_manual_locked_constraints(x, data)

Arguments

x	`problem()` (i.e. `ConservationProblem`) object.
data	`data.frame` or `tibble::tibble()` object. See the Data format section for more information.

Value

Object (i.e. ConservationProblem) with the constraints added to it.

Data format

The argument to data must contain the following fields (columns):

pu: integer planning unit identifier.
zone: character names of zones. Note that this argument is optional for arguments to x that contain a single zone.
status: numeric values indicating how much of each planning unit should be allocated to each zone in the solution. For example, the numeric values could be binary values (i.e. zero or one) for problems containing binary-type decision variables (using the add_binary_decisions() function). Alternatively, the numeric values could be proportions (e.g. 0.5) for problems containing proportion-type decision variables (using the add_proportion_decisions()).

Examples

# set seed for reproducibility
set.seed(500)

# load data
data(sim_pu_polygons, sim_features, sim_pu_zones_polygons,
     sim_features_zones)

# create minimal problem
p1 <- problem(sim_pu_polygons, sim_features, "cost") %>%
      add_min_set_objective() %>%
      add_relative_targets(0.2) %>%
      add_binary_decisions() %>%
      add_default_solver(verbose = FALSE)

# create problem with locked in constraints using add_locked_constraints
p2 <- p1 %>% add_locked_in_constraints("locked_in")

# create identical problem using add_manual_locked_constraints
locked_data <- data.frame(pu = which(sim_pu_polygons$locked_in),
                          status = 1)

p3 <- p1 %>% add_manual_locked_constraints(locked_data)
# \dontrun{
# solve problems
s1 <- solve(p1)
#> $LogToConsole
#> [1] 0
#> 
#> $LogFile
#> [1] ""
#> 
#> $Presolve
#> [1] 2
#> 
#> $MIPGap
#> [1] 0.1
#> 
#> $TimeLimit
#> [1] 2147483647
#> 
#> $Threads
#> [1] 1
#> 
#> $NumericFocus
#> [1] 0
#> 
s2 <- solve(p2)
#> $LogToConsole
#> [1] 0
#> 
#> $LogFile
#> [1] ""
#> 
#> $Presolve
#> [1] 2
#> 
#> $MIPGap
#> [1] 0.1
#> 
#> $TimeLimit
#> [1] 2147483647
#> 
#> $Threads
#> [1] 1
#> 
#> $NumericFocus
#> [1] 0
#> 
s3 <- solve(p3)
#> $LogToConsole
#> [1] 0
#> 
#> $LogFile
#> [1] ""
#> 
#> $Presolve
#> [1] 2
#> 
#> $MIPGap
#> [1] 0.1
#> 
#> $TimeLimit
#> [1] 2147483647
#> 
#> $Threads
#> [1] 1
#> 
#> $NumericFocus
#> [1] 0
#> 

# plot solutions
par(mfrow = c(1,3), mar = c(0, 0, 4.1, 0))
plot(s1, main = "none locked in")
plot(s1[s1$solution_1 == 1, ], col = "darkgreen", add = TRUE)

plot(s2, main = "add_locked_in_constraints")
plot(s2[s2$solution_1 == 1, ], col = "darkgreen", add = TRUE)

plot(s3, main = "add_manual_constraints")
plot(s3[s3$solution_1 == 1, ], col = "darkgreen", add = TRUE)
# }
# create minimal problem with multiple zones
p4 <- problem(sim_pu_zones_polygons, sim_features_zones,
              c("cost_1", "cost_2", "cost_3")) %>%
      add_min_set_objective() %>%
      add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5,
                                  ncol = 3)) %>%
      add_binary_decisions() %>%
      add_default_solver(verbose = FALSE)

# create data.frame with the following constraints:
# planning units 1, 2, and 3 must be allocated to zone 1 in the solution
# planning units 4, and 5 must be allocated to zone 2 in the solution
# planning units 8 and 9 must not be allocated to zone 3 in the solution
locked_data2 <- data.frame(pu = c(1, 2, 3, 4, 5, 8, 9),
                           zone = c(rep("zone_1", 3), rep("zone_2", 2),
                                    rep("zone_3", 2)),
                           status = c(rep(1, 5), rep(0, 2)))

# print locked constraint data
print(locked_data2)
#>   pu   zone status
#> 1  1 zone_1      1
#> 2  2 zone_1      1
#> 3  3 zone_1      1
#> 4  4 zone_2      1
#> 5  5 zone_2      1
#> 6  8 zone_3      0
#> 7  9 zone_3      0

# create problem with added constraints
p5 <- p4 %>% add_manual_locked_constraints(locked_data2)
# \dontrun{
# solve problem
s4 <- solve(p4)
#> $LogToConsole
#> [1] 0
#> 
#> $LogFile
#> [1] ""
#> 
#> $Presolve
#> [1] 2
#> 
#> $MIPGap
#> [1] 0.1
#> 
#> $TimeLimit
#> [1] 2147483647
#> 
#> $Threads
#> [1] 1
#> 
#> $NumericFocus
#> [1] 0
#> 
s5 <- solve(p5)
#> $LogToConsole
#> [1] 0
#> 
#> $LogFile
#> [1] ""
#> 
#> $Presolve
#> [1] 2
#> 
#> $MIPGap
#> [1] 0.1
#> 
#> $TimeLimit
#> [1] 2147483647
#> 
#> $Threads
#> [1] 1
#> 
#> $NumericFocus
#> [1] 0
#> 

# create two new columns representing the zone id that each planning unit
# was allocated to in the two solutions
s4$solution <- category_vector(s4@data[, c("solution_1_zone_1",
                                           "solution_1_zone_2",
                                           "solution_1_zone_3")])
s4$solution <- factor(s4$solution)

s4$solution_locked <- category_vector(s5@data[, c("solution_1_zone_1",
                                                  "solution_1_zone_2",
                                                  "solution_1_zone_3")])
s4$solution_locked <- factor(s4$solution_locked)

# plot solutions
spplot(s4, zcol = c("solution", "solution_locked"), axes = FALSE,
       box = FALSE)
# }

Arguments

Value

Data format

See also

Examples