Add constraints to a project prioritization problem() to ensure
that specific actions are not prioritized for funding in the solution. For
example, it may be desirable to lock out specific actions to examine their
importance to the optimal funding scheme.
add_locked_out_constraints(x, locked_out)
# S4 method for ProjectProblem,numeric
add_locked_out_constraints(x, locked_out)
# S4 method for ProjectProblem,logical
add_locked_out_constraints(x, locked_out)
# S4 method for ProjectProblem,character
add_locked_out_constraints(x, locked_out)ProjectProblem object.
Object that determines which planning units that should be locked out. See the Details section for more information.
# load data
data(sim_projects, sim_features, sim_actions)
# update "locked_out" column to lock out action "F2_action"
sim_actions$locked_out <- c(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE)
# print action data
print(sim_actions)
#> # A tibble: 6 × 4
#> name cost locked_in locked_out
#> <chr> <dbl> <lgl> <lgl>
#> 1 F1_action 94.4 FALSE FALSE
#> 2 F2_action 101. FALSE TRUE
#> 3 F3_action 103. TRUE FALSE
#> 4 F4_action 99.2 FALSE FALSE
#> 5 F5_action 99.9 FALSE FALSE
#> 6 baseline_action 0 FALSE FALSE
# build problem with maximum richness objective and $150 budget
p1 <- problem(sim_projects, sim_actions, sim_features,
"name", "success", "name", "cost", "name") %>%
add_max_richness_objective(budget = 150) %>%
add_binary_decisions()
# print problem
print(p1)
#> Project Prioritization Problem
#> actions F1_action, F2_action, F3_action, ... (6 actions)
#> projects F1_project, F2_project, F3_project, ... (6 projects)
#> features F1, F2, F3, ... (5 features)
#> action costs: min: 0, max: 103.22583
#> project success: min: 0.81379, max: 1
#> objective: Maximum richness objective [budget (150)]
#> targets: none
#> weights: default
#> decisions Binary decision
#> constraints: <none>
#> solver: default
# build another problem, and lock out the second action using numeric inputs
p2 <- p1 %>%
add_locked_out_constraints(c(2))
# print problem
print(p2)
#> Project Prioritization Problem
#> actions F1_action, F2_action, F3_action, ... (6 actions)
#> projects F1_project, F2_project, F3_project, ... (6 projects)
#> features F1, F2, F3, ... (5 features)
#> action costs: min: 0, max: 103.22583
#> project success: min: 0.81379, max: 1
#> objective: Maximum richness objective [budget (150)]
#> targets: none
#> weights: default
#> decisions Binary decision
#> constraints: <Manually locked actions [1 locked units]>
#> solver: default
# build another problem, and lock out the actions using logical inputs
# (i.e. TRUE/FALSE values) from the sim_actions table
p3 <- p1 %>%
add_locked_out_constraints(sim_actions$locked_out)
# print problem
print(p3)
#> 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: Maximum richness objective [budget (150)]
#> targets: none
#> weights: default
#> decisions Binary decision
#> constraints: <Manually locked actions [1 locked units]>
#> solver: default
# build another problem, and lock out the actions using the column name
# "locked_out" in the sim_actions table
# the sim_actions table
p4 <- p1 %>%
add_locked_out_constraints("locked_out")
# print problem
print(p4)
#> 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: Maximum richness objective [budget (150)]
#> targets: none
#> weights: default
#> decisions Binary decision
#> constraints: <Manually locked actions [1 locked units]>
#> solver: default
# \dontrun{
# solve problems
s1 <- solve(p1)
#> 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 47 rows, 47 columns and 102 nonzeros
#> Model fingerprint: 0xf97d9094
#> Variable types: 0 continuous, 42 integer (42 binary)
#> Semi-Variable types: 5 continuous, 0 integer
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+02]
#> Objective range [1e+00, 1e+00]
#> Bounds range [1e+00, 1e+00]
#> RHS range [1e+00, 2e+02]
#> Found heuristic solution: objective 1.4456093
#> Presolve removed 16 rows and 12 columns
#> Presolve time: 0.00s
#> Presolved: 31 rows, 35 columns, 65 nonzeros
#> Variable types: 0 continuous, 35 integer (35 binary)
#> Root relaxation presolved: 31 rows, 35 columns, 65 nonzeros
#>
#>
#> Root relaxation: objective 1.680145e+00, 11 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 1.6801450 1.68015 0.00% - 0s
#>
#> Explored 1 nodes (11 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#>
#> Solution count 1: 1.68015
#>
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 1.680145013696e+00, best bound 1.680145013696e+00, gap 0.0000%
s2 <- solve(p2)
#> 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 47 rows, 47 columns and 102 nonzeros
#> Model fingerprint: 0x80197d20
#> Variable types: 0 continuous, 42 integer (42 binary)
#> Semi-Variable types: 5 continuous, 0 integer
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+02]
#> Objective range [1e+00, 1e+00]
#> Bounds range [1e+00, 1e+00]
#> RHS range [1e+00, 2e+02]
#> Found heuristic solution: objective 1.4456093
#> Presolve removed 22 rows and 19 columns
#> Presolve time: 0.00s
#> Presolved: 25 rows, 28 columns, 52 nonzeros
#> Variable types: 0 continuous, 28 integer (28 binary)
#> Root relaxation presolved: 25 rows, 28 columns, 52 nonzeros
#>
#>
#> Root relaxation: objective 1.575441e+00, 9 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 1.5754408 1.57544 0.00% - 0s
#>
#> Explored 1 nodes (9 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#>
#> Solution count 1: 1.57544
#>
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 1.575440809243e+00, best bound 1.575440809243e+00, gap 0.0000%
s3 <- solve(p3)
#> 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 47 rows, 47 columns and 102 nonzeros
#> Model fingerprint: 0x80197d20
#> Variable types: 0 continuous, 42 integer (42 binary)
#> Semi-Variable types: 5 continuous, 0 integer
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+02]
#> Objective range [1e+00, 1e+00]
#> Bounds range [1e+00, 1e+00]
#> RHS range [1e+00, 2e+02]
#> Found heuristic solution: objective 1.4456093
#> Presolve removed 22 rows and 19 columns
#> Presolve time: 0.00s
#> Presolved: 25 rows, 28 columns, 52 nonzeros
#> Variable types: 0 continuous, 28 integer (28 binary)
#> Root relaxation presolved: 25 rows, 28 columns, 52 nonzeros
#>
#>
#> Root relaxation: objective 1.575441e+00, 9 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 1.5754408 1.57544 0.00% - 0s
#>
#> Explored 1 nodes (9 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#>
#> Solution count 1: 1.57544
#>
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 1.575440809243e+00, best bound 1.575440809243e+00, gap 0.0000%
s4 <- solve(p4)
#> 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 47 rows, 47 columns and 102 nonzeros
#> Model fingerprint: 0x80197d20
#> Variable types: 0 continuous, 42 integer (42 binary)
#> Semi-Variable types: 5 continuous, 0 integer
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+02]
#> Objective range [1e+00, 1e+00]
#> Bounds range [1e+00, 1e+00]
#> RHS range [1e+00, 2e+02]
#> Found heuristic solution: objective 1.4456093
#> Presolve removed 22 rows and 19 columns
#> Presolve time: 0.00s
#> Presolved: 25 rows, 28 columns, 52 nonzeros
#> Variable types: 0 continuous, 28 integer (28 binary)
#> Root relaxation presolved: 25 rows, 28 columns, 52 nonzeros
#>
#>
#> Root relaxation: objective 1.575441e+00, 9 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 1.5754408 1.57544 0.00% - 0s
#>
#> Explored 1 nodes (9 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#>
#> Solution count 1: 1.57544
#>
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 1.575440809243e+00, best bound 1.575440809243e+00, gap 0.0000%
# print the actions selected for funding in each of the solutions
print(s1[, sim_actions$name])
#> # A tibble: 1 × 6
#> F1_action F2_action F3_action F4_action F5_action baseline_action
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 1 0 0 0 1
print(s2[, sim_actions$name])
#> # A tibble: 1 × 6
#> F1_action F2_action F3_action F4_action F5_action baseline_action
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0 0 0 1
print(s3[, sim_actions$name])
#> # A tibble: 1 × 6
#> F1_action F2_action F3_action F4_action F5_action baseline_action
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0 0 0 1
print(s4[, sim_actions$name])
#> # A tibble: 1 × 6
#> F1_action F2_action F3_action F4_action F5_action baseline_action
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0 0 0 1
# }