Add locked out action constraints
Source:R/add_locked_out_action_constraints.R
add_locked_out_action_constraints.RdAdd constraints to a project prioritization problem to ensure that particular actions are not selected for funding by the solution. For example, it may be desirable to lock out specific actions to examine their importance to the optimal funding scheme.
Usage
add_locked_out_action_constraints(x, locked_out)
# S4 method for class 'ProjectProblem,numeric'
add_locked_out_action_constraints(x, locked_out)
# S4 method for class 'ProjectProblem,logical'
add_locked_out_action_constraints(x, locked_out)
# S4 method for class 'ProjectProblem,character'
add_locked_out_action_constraints(x, locked_out)Arguments
- x
problem()object.- locked_out
Object that determines which planning units that should be locked out. See the Details section for more information.
Examples
# 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 weighted sum objective and $150 budget
p1 <-
problem(
sim_projects, sim_actions, sim_features,
"name", "success", "name", "cost", "name"
) %>%
add_max_wtd_sum_objective(budget = 150) %>%
add_binary_decisions()
# print problem
print(p1)
#> Project Prioritization Problem
#> actions: F1_action, F2_action, F3_action, ... (6 actions)
#> projects: F1_project, F2_project, F3_project, ... (6 projects)
#> features: F1, F2, F3, ... (5 features)
#> action costs: continuous values (between 0 and 103.226)
#> project success: proportion values (between 0.814 and 1)
#> objective: maximum weighted sum objective
#> targets: none specified
#> weights: none specified
#> constraints: none specified
#> decisions: binary decision
#> solver: none specified
# build another problem, and lock out the second action using numeric inputs
p2 <- p1 %>% add_locked_out_action_constraints(c(2))
# print problem
print(p2)
#> Project Prioritization Problem
#> actions: F1_action, F2_action, F3_action, ... (6 actions)
#> projects: F1_project, F2_project, F3_project, ... (6 projects)
#> features: F1, F2, F3, ... (5 features)
#> action costs: continuous values (between 0 and 103.226)
#> project success: proportion values (between 0.814 and 1)
#> objective: maximum weighted sum objective
#> targets: none specified
#> weights: none specified
#> constraints: manually locked actions
#> decisions: binary decision
#> solver: none specified
# build another problem, and lock out the actions using logical inputs
# (i.e., TRUE/FALSE values) from the sim_actions table
p3 <- p1 %>% add_locked_out_action_constraints(sim_actions$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: continuous values (between 0 and 103.226)
#> project success: proportion values (between 0.814 and 1)
#> objective: maximum weighted sum objective
#> targets: none specified
#> weights: none specified
#> constraints: manually locked actions
#> decisions: binary decision
#> solver: none specified
# build another problem, and lock out the actions using the column name
# "locked_out" in the sim_actions table
p4 <- p1 %>% add_locked_out_action_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: continuous values (between 0 and 103.226)
#> project success: proportion values (between 0.814 and 1)
#> objective: maximum weighted sum objective
#> targets: none specified
#> weights: none specified
#> constraints: manually locked actions
#> decisions: binary decision
#> solver: none specified
# solve problems
s1 <- solve(p1)
#> Set parameter Username
#> Set parameter LicenseID to value 2806834
#> Set parameter TimeLimit to value 2147483647
#> Set parameter MIPGap to value 0
#> Set parameter ScaleFlag to value 2
#> Set parameter NumericFocus to value 1
#> Set parameter Presolve to value 2
#> Set parameter Threads to value 1
#> Set parameter PoolSolutions to value 1
#> Set parameter PoolSearchMode to value 2
#> Academic license - for non-commercial use only - expires 2027-04-14
#> Gurobi Optimizer version 13.0.1 build v13.0.1rc0 (linux64 - "Ubuntu 24.04.2 LTS")
#>
#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]
#> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads
#>
#> Non-default parameters:
#> TimeLimit 2147483647
#> MIPGap 0
#> ScaleFlag 2
#> NumericFocus 1
#> Presolve 2
#> Threads 1
#> PoolSolutions 1
#> PoolSearchMode 2
#>
#> Optimize a model with 27 rows, 27 columns and 62 nonzeros (Max)
#> Model fingerprint: 0x561c9c42
#> Model has 5 linear objective coefficients
#> Variable types: 5 continuous, 22 integer (22 binary)
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+02]
#> Objective range [1e+00, 1e+00]
#> Bounds range [5e-01, 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: 11 rows, 15 columns, 25 nonzeros
#> Variable types: 0 continuous, 15 integer (15 binary)
#> Root relaxation presolved: 11 rows, 15 columns, 25 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
#> No other solutions better than 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)
#> Set parameter Username
#> Set parameter LicenseID to value 2806834
#> Set parameter TimeLimit to value 2147483647
#> Set parameter MIPGap to value 0
#> Set parameter ScaleFlag to value 2
#> Set parameter NumericFocus to value 1
#> Set parameter Presolve to value 2
#> Set parameter Threads to value 1
#> Set parameter PoolSolutions to value 1
#> Set parameter PoolSearchMode to value 2
#> Academic license - for non-commercial use only - expires 2027-04-14
#> Gurobi Optimizer version 13.0.1 build v13.0.1rc0 (linux64 - "Ubuntu 24.04.2 LTS")
#>
#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]
#> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads
#>
#> Non-default parameters:
#> TimeLimit 2147483647
#> MIPGap 0
#> ScaleFlag 2
#> NumericFocus 1
#> Presolve 2
#> Threads 1
#> PoolSolutions 1
#> PoolSearchMode 2
#>
#> Optimize a model with 27 rows, 27 columns and 62 nonzeros (Max)
#> Model fingerprint: 0xfd5c060b
#> Model has 5 linear objective coefficients
#> Variable types: 5 continuous, 22 integer (22 binary)
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+02]
#> Objective range [1e+00, 1e+00]
#> Bounds range [5e-01, 1e+00]
#> RHS range [1e+00, 2e+02]
#>
#> Found heuristic solution: objective 1.4456093
#> Presolve removed 18 rows and 15 columns
#> Presolve time: 0.00s
#> Presolved: 9 rows, 12 columns, 20 nonzeros
#> Variable types: 0 continuous, 12 integer (12 binary)
#> Root relaxation presolved: 9 rows, 12 columns, 20 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
#> No other solutions better than 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)
#> Set parameter Username
#> Set parameter LicenseID to value 2806834
#> Set parameter TimeLimit to value 2147483647
#> Set parameter MIPGap to value 0
#> Set parameter ScaleFlag to value 2
#> Set parameter NumericFocus to value 1
#> Set parameter Presolve to value 2
#> Set parameter Threads to value 1
#> Set parameter PoolSolutions to value 1
#> Set parameter PoolSearchMode to value 2
#> Academic license - for non-commercial use only - expires 2027-04-14
#> Gurobi Optimizer version 13.0.1 build v13.0.1rc0 (linux64 - "Ubuntu 24.04.2 LTS")
#>
#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]
#> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads
#>
#> Non-default parameters:
#> TimeLimit 2147483647
#> MIPGap 0
#> ScaleFlag 2
#> NumericFocus 1
#> Presolve 2
#> Threads 1
#> PoolSolutions 1
#> PoolSearchMode 2
#>
#> Optimize a model with 27 rows, 27 columns and 62 nonzeros (Max)
#> Model fingerprint: 0xfd5c060b
#> Model has 5 linear objective coefficients
#> Variable types: 5 continuous, 22 integer (22 binary)
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+02]
#> Objective range [1e+00, 1e+00]
#> Bounds range [5e-01, 1e+00]
#> RHS range [1e+00, 2e+02]
#>
#> Found heuristic solution: objective 1.4456093
#> Presolve removed 18 rows and 15 columns
#> Presolve time: 0.00s
#> Presolved: 9 rows, 12 columns, 20 nonzeros
#> Variable types: 0 continuous, 12 integer (12 binary)
#> Root relaxation presolved: 9 rows, 12 columns, 20 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
#> No other solutions better than 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)
#> Set parameter Username
#> Set parameter LicenseID to value 2806834
#> Set parameter TimeLimit to value 2147483647
#> Set parameter MIPGap to value 0
#> Set parameter ScaleFlag to value 2
#> Set parameter NumericFocus to value 1
#> Set parameter Presolve to value 2
#> Set parameter Threads to value 1
#> Set parameter PoolSolutions to value 1
#> Set parameter PoolSearchMode to value 2
#> Academic license - for non-commercial use only - expires 2027-04-14
#> Gurobi Optimizer version 13.0.1 build v13.0.1rc0 (linux64 - "Ubuntu 24.04.2 LTS")
#>
#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]
#> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads
#>
#> Non-default parameters:
#> TimeLimit 2147483647
#> MIPGap 0
#> ScaleFlag 2
#> NumericFocus 1
#> Presolve 2
#> Threads 1
#> PoolSolutions 1
#> PoolSearchMode 2
#>
#> Optimize a model with 27 rows, 27 columns and 62 nonzeros (Max)
#> Model fingerprint: 0xfd5c060b
#> Model has 5 linear objective coefficients
#> Variable types: 5 continuous, 22 integer (22 binary)
#> Coefficient statistics:
#> Matrix range [9e-02, 1e+02]
#> Objective range [1e+00, 1e+00]
#> Bounds range [5e-01, 1e+00]
#> RHS range [1e+00, 2e+02]
#>
#> Found heuristic solution: objective 1.4456093
#> Presolve removed 18 rows and 15 columns
#> Presolve time: 0.00s
#> Presolved: 9 rows, 12 columns, 20 nonzeros
#> Variable types: 0 continuous, 12 integer (12 binary)
#> Root relaxation presolved: 9 rows, 12 columns, 20 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
#> No other solutions better than 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$name])
#> # A tibble: 1 × 6
#> F1_action F2_action F3_action F4_action F5_action baseline_action
#> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 FALSE TRUE FALSE FALSE FALSE TRUE
print(s2[, sim_actions$name])
#> # A tibble: 1 × 6
#> F1_action F2_action F3_action F4_action F5_action baseline_action
#> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE FALSE FALSE FALSE FALSE TRUE
print(s3[, sim_actions$name])
#> # A tibble: 1 × 6
#> F1_action F2_action F3_action F4_action F5_action baseline_action
#> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE FALSE FALSE FALSE FALSE TRUE
print(s4[, sim_actions$name])
#> # A tibble: 1 × 6
#> F1_action F2_action F3_action F4_action F5_action baseline_action
#> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE FALSE FALSE FALSE FALSE TRUE