solve {oppr}

Solve

Description

Solve a conservation planning problem().

Usage

## S4 method for signature 'OptimizationProblem,Solver'
solve(a, b, ...)

## S4 method for signature 'ProjectProblem,missing'
solve(a, b, ...)

Arguments

a

ProjectProblem or an OptimizationProblem object.

b

Solver object. Not used if a is an ProjectProblem object.

...

arguments passed to compile().

Value

The type of object returned from this function depends on the argument to a. If the argument to a is an OptimizationProblem object, then the solution is returned as a list containing the prioritization and additional information (e.g. run time, solver status). On the other hand, if the argument to a is an ProjectProblem object, then a tibble::tibble() table object will be returned. In this table, each row row corresponds to a different solution and each column describes a different property or result associated with each solution:

"solution"

integer solution identifier.

"status"

character describing each solution. For example, is the solution optimal, suboptimal, or was it returned because the solver ran out of time?

"obj"

numeric objective value for each solution. This is calculated using the objective function defined for the argument to x.

"cost"

numeric total cost associated with each solution.

x$action_names()

numeric column for each action indicating if they were funded in each solution or not.

x$project_names()

numeric column for each project indicating if it was completely funded (with a value of 1) or not (with a value of 0).

x$feature_names()

numeric column for each feature indicating the probability that it will persist into the future given each solution.

See Also

problem(), solution_statistics(), solvers.

Examples

# load data
data(sim_projects, sim_features, sim_actions)

# print project data
print(sim_projects)

# print action data
print(sim_features)

# print feature data
print(sim_actions)

# build problem
p <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_max_richness_objective(budget = 400) %>%
     add_feature_weights("weight") %>%
     add_binary_decisions()

# print problem
print(p)

## Not run: 
# solve problem
s <- solve(p)

# print output
print(s)

# print the solver status
print(s$obj)

# print the objective value
print(s$obj)

# print the solution cost
print(s$cost)

# print which actions are funded in the solution
s[, sim_actions$name, drop = FALSE]

# print the expected probability of persistence for each feature
# if the solution were implemented
s[, sim_features$name, drop = FALSE]

## End(Not run)