oppr (version 1.0.4)

targets: Targets

Description

Targets are used to specify the minimum probability of persistence required for each feature. Please note that only some objectives require targets, and attempting to solve a problem that requires targets will throw an error if targets are not supplied, and attempting to solve a problem that does not require targets will throw a warning if targets are supplied.

Details

The following functions can be used to specify targets for a project prioritization problem():

add_relative_targets()

Set targets as a proportion (between 0 and 1) of the maximum probability of persistence associated with the best project for each feature. For instance, if the best project for a feature has an 80% probability of persisting, setting a 50% (i.e. 0.5) relative target will correspond to a 40% threshold probability of persisting.

add_absolute_targets()

Set targets by specifying exactly what probability of persistence is required for each feature. For instance, setting an absolute target of 10% (i.e. 0.1) corresponds to a threshold 10% probability of persisting.

add_manual_targets()

Set targets by manually specifying all the required information for each target.

See Also

constraints, decisions, objectives, problem(), solvers.

Examples

# load data
data(sim_projects, sim_features, sim_actions)

# build problem with minimum set objective and targets that require each
# feature to have a 30% chance of persisting into the future
p1 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
      add_min_set_objective() %>%
      add_absolute_targets(0.3) %>%
      add_binary_decisions()

# print problem
print(p1)

# build problem with minimum set objective and targets that require each
# feature to have a level of persistence that is greater than or equal to
# 30% of the best project for conserving it
p2 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
      add_min_set_objective() %>%
      add_relative_targets(0.3) %>%
      add_binary_decisions()

# print problem
print(p2)

if (FALSE) {
# solve problems
s1 <- solve(p1)
s2 <- solve(p2)

# print solutions
print(s1)
print(s2)

# plot solutions
plot(p1, s1)
plot(p2, s2)
}