Add manually specified locked constraints for actions

Add constraints to a project prioritization problem to ensure that particular actions are selected, or not selected, for funding by the solution. This function offers more fine-grained control than the add_locked_in_action_constraints() and add_locked_out_action_constraints() functions.

Usage

add_manual_locked_action_constraints(x, locked)

# S4 method for class 'ProjectProblem,data.frame'
add_manual_locked_action_constraints(x, locked)

# S4 method for class 'ProjectProblem,tbl_df'
add_manual_locked_action_constraints(x, locked)

Arguments

x

problem() object.

locked

data.frame or tibble::tibble() object. See the Details section for more information.

Value

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

Details

The argument to locked must contain the following columns.

"action"

character values with action names.

"status"

numeric values indicating if actions should be selected for funding (with a value of 1) or not (with a value of zero).

See also

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

Examples

# load data
data(sim_projects, sim_features, sim_actions)

# create data frame with locked statuses
locked_data <- data.frame(
  action = sim_actions$name[1:2],
  status = c(0, 1)
)

# print locked statuses
print(locked_data)
#>      action status
#> 1 F1_action      0
#> 2 F2_action      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_max_wtd_sum_objective(budget = 500) %>%
  add_manual_locked_action_constraints(locked_data) %>%
  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:    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 problem
s <- solve(p)
#> 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: 0x198b718c
#> 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, 5e+02]
#> 
#> Found heuristic solution: objective 1.4456093
#> Presolve removed 20 rows and 16 columns
#> Presolve time: 0.00s
#> Presolved: 7 rows, 11 columns, 14 nonzeros
#> Variable types: 0 continuous, 11 integer (11 binary)
#> Root relaxation presolved: 7 rows, 11 columns, 14 nonzeros
#> 
#> 
#> Root relaxation: objective 2.910246e+00, 7 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       2.9102457    2.91025  0.00%     -    0s
#> 
#> Explored 1 nodes (7 simplex iterations) in 0.00 seconds (0.00 work units)
#> Thread count was 1 (of 8 available processors)
#> 
#> Solution count 1: 2.91025 
#> No other solutions better than 2.91025
#> 
#> Optimal solution found (tolerance 0.00e+00)
#> Best objective 2.910245655750e+00, best bound 2.910245655750e+00, gap 0.0000%

# print solution
print(s)
#> # A tibble: 1 × 21
#>   solution status   cost   obj F1_action F2_action F3_action F4_action F5_action
#>      <int> <chr>   <dbl> <dbl> <lgl>     <lgl>     <lgl>     <lgl>     <lgl>    
#> 1        1 OPTIMAL  403.  2.91 FALSE     TRUE      TRUE      TRUE      TRUE     
#> # ℹ 12 more variables: baseline_action <lgl>, F1_project <lgl>,
#> #   F2_project <lgl>, F3_project <lgl>, F4_project <lgl>, F5_project <lgl>,
#> #   baseline_project <lgl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>