Set targets for a project prioritization problem() by manually
specifying all the required information for each target. This function
is useful because it can be used to customize all aspects of a target. For
most cases, targets can be specified using the
add_absolute_targets() and add_relative_targets()
functions. However, this function can be used to mix absolute and
relative targets for different features.
add_manual_targets(x, targets)
# S4 method for ProjectProblem,data.frame
add_manual_targets(x, targets)
# S4 method for ProjectProblem,tbl_df
add_manual_targets(x, targets)ProjectProblem object.
data.frame or tibble::tibble() object. See
the Details section for more information.
ProjectProblem object with the targets added to it.
Targets are used to specify the minimum probability of persistence
for each feature in solutions. For minimum set objectives
(i.e. add_min_set_objective(), these targets
specify the minimum probability of persistence required for each species
in the solution. And for budget constrained objectives that use targets
(i.e. add_max_targets_met_objective()), these targets
specify the minimum threshold probability of persistence that needs to be
achieved to count the benefits for conserving these species.
Please note that attempting to solve problems with objectives that require
targets without specifying targets will throw an error.
The targets argument should contain the following columns:
"feature"character name of features in argument
to x.
"type"character describing the type of target.
Acceptable values include "absolute" and "relative".
These values correspond to add_absolute_targets(),
and add_relative_targets() respectively.
"sense"character sense of the target. The
only acceptable value currently supported is: ">=". This field
(column) is optional and if it is missing then target senses will
default to ">=" values.
"target"numeric target threshold.
# load data
data(sim_projects, sim_features, sim_actions)
# create data frame with targets
targets <- data.frame(feature = sim_features$name,
type = "absolute",
target = 0.1)
# print targets
print(targets)
#> feature type target
#> 1 F1 absolute 0.1
#> 2 F2 absolute 0.1
#> 3 F3 absolute 0.1
#> 4 F4 absolute 0.1
#> 5 F5 absolute 0.1
# build problem with minimum set objective and targets that require each
# feature to have a 30% chance of persisting into the future
p <- problem(sim_projects, sim_actions, sim_features,
"name", "success", "name", "cost", "name") %>%
add_min_set_objective() %>%
add_manual_targets(targets) %>%
add_binary_decisions()
# print problem
print(p)
#> Project Prioritization Problem
#> actions F1_action, F2_action, F3_action, ... (6 actions)
#> projects F1_project, F2_project, F3_project, ... (6 projects)
#> features F1, F2, F3, ... (5 features)
#> action costs: min: 0, max: 103.22583
#> project success: min: 0.81379, max: 1
#> objective: Minimum set objective
#> targets: Absolute targets [targets (min: 0.1, max: 0.1)]
#> weights: default
#> decisions Binary decision
#> constraints: <none>
#> solver: default
# \dontrun{
# solve problem
s <- solve(p)
#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)
#> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads
#> Optimize a model with 46 rows, 42 columns and 92 nonzeros
#> Model fingerprint: 0x5a3c452c
#> Variable types: 0 continuous, 42 integer (42 binary)
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+00]
#> Objective range [9e+01, 1e+02]
#> Bounds range [1e+00, 1e+00]
#> RHS range [1e-01, 1e+00]
#> Found heuristic solution: objective 304.1251127
#> Presolve removed 17 rows and 8 columns
#> Presolve time: 0.00s
#> Presolved: 29 rows, 34 columns, 58 nonzeros
#> Variable types: 0 continuous, 34 integer (34 binary)
#> Root relaxation presolved: 29 rows, 34 columns, 58 nonzeros
#>
#>
#> Root relaxation: objective 1.032258e+02, 6 iterations, 0.00 seconds (0.00 work units)
#>
#> Nodes | Current Node | Objective Bounds | Work
#> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
#>
#> * 0 0 0 103.2258290 103.22583 0.00% - 0s
#>
#> Explored 1 nodes (6 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#>
#> Solution count 1: 103.226
#>
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 1.032258290263e+02, best bound 1.032258290263e+02, gap 0.0000%
# print solution
print(s)
#> # A tibble: 1 × 21
#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 OPTIMAL 103. 103. 0 0 1 0 0 1
#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>,
#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>,
#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>,
#> # and abbreviated variable names ¹F2_action, ²F3_action, ³F4_action,
#> # ⁴F5_action, ⁵baseline_action
# }