Add locked in action constraints

Add constraints to a project prioritization problem to ensure that particular actions are selected for funding by the solution. For example, it may be desirable to lock in actions for conserving culturally or taxonomically important species.

Usage

add_locked_in_action_constraints(x, locked_in)

# S4 method for class 'ProjectProblem,numeric'
add_locked_in_action_constraints(x, locked_in)

# S4 method for class 'ProjectProblem,logical'
add_locked_in_action_constraints(x, locked_in)

# S4 method for class 'ProjectProblem,character'
add_locked_in_action_constraints(x, locked_in)

Arguments

x

problem() object.

locked_in

Object that determines which planning units that should be locked in. See the Details section for more information.

Value

A problem() object with the constraints added to it.

Details

The locked actions can be specified in several different ways:

integer vector

Each values specifies the index for an action that should be locked when generating solutions (i.e., row numbers of the actions in the argument to actions in problem()).

logical vector

Each value (i.e., TRUE and/or FALSE) indicates if an action should be locked (or not) when generating the solution. These logical values should correspond to each row in the argument to actions in problem().

character value

The value specifies the name of a column in the action data (i.e., argument to actions in problem()). The column must have logical (i.e., TRUE and/or FALSE) values, and these values are used to indicate which actions are locked (or not).

See also

See constraints for an overview of functions for adding constraints.

Other constraints: add_locked_in_project_constraints(), add_locked_out_action_constraints(), add_locked_out_project_constraints(), add_manual_locked_action_constraints(), add_manual_locked_project_constraints()

Examples

# load data
data(sim_projects, sim_features, sim_actions)

# 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     FALSE     
#> 3 F3_action       103.  TRUE      FALSE     
#> 4 F4_action        99.2 FALSE     FALSE     
#> 5 F5_action        99.9 FALSE     TRUE      
#> 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 in the 3rd action using numeric inputs
p2 <- p1 %>% add_locked_in_action_constraints(c(3))

# 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:     locked in actions
#> decisions:       binary decision
#> solver:          none specified

# build another problem, and lock in the actions using logical inputs from
# the sim_actions table
p3 <- p1 %>% add_locked_in_action_constraints(sim_actions$locked_in)

# 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:     locked in actions
#> decisions:       binary decision
#> solver:          none specified

# build another problem, and lock in the actions using the column name
# "locked_in" in the sim_actions table
p4 <- p1 %>% add_locked_in_action_constraints("locked_in")

# 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:     locked in 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: 0x55efe312
#> 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 26 rows and 25 columns
#> Presolve time: 0.00s
#> Presolved: 1 rows, 2 columns, 2 nonzeros
#> Variable types: 0 continuous, 2 integer (2 binary)
#> 
#> Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#> 
#> Solution count 1: 1.44561 
#> No other solutions better than 1.44561
#> 
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 1.445609277954e+00, best bound 1.445609277954e+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: 0x55efe312
#> 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 26 rows and 25 columns
#> Presolve time: 0.00s
#> Presolved: 1 rows, 2 columns, 2 nonzeros
#> Variable types: 0 continuous, 2 integer (2 binary)
#> 
#> Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#> 
#> Solution count 1: 1.44561 
#> No other solutions better than 1.44561
#> 
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 1.445609277954e+00, best bound 1.445609277954e+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: 0x55efe312
#> 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 26 rows and 25 columns
#> Presolve time: 0.00s
#> Presolved: 1 rows, 2 columns, 2 nonzeros
#> Variable types: 0 continuous, 2 integer (2 binary)
#> 
#> Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#> 
#> Solution count 1: 1.44561 
#> No other solutions better than 1.44561
#> 
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 1.445609277954e+00, best bound 1.445609277954e+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 FALSE     FALSE     TRUE      FALSE     FALSE     TRUE           
print(s3[, sim_actions$name])
#> # A tibble: 1 × 6
#>   F1_action F2_action F3_action F4_action F5_action baseline_action
#>   <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>          
#> 1 FALSE     FALSE     TRUE      FALSE     FALSE     TRUE           
print(s4[, sim_actions$name])
#> # A tibble: 1 × 6
#>   F1_action F2_action F3_action F4_action F5_action baseline_action
#>   <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>          
#> 1 FALSE     FALSE     TRUE      FALSE     FALSE     TRUE