prioritizr (version 3.0.4)

objectives: Problem objective

Description

A problem objective is used to specify the overall goal of the problem. All conservation planning problems involve minimizing or maximizing some kind of objective. For instance, the planner may require a solution that conserves enough habitat for each species while minimizing the overall cost of the reserve network. Alternatively, the planner may require a solution that maximizes the number of conserved species while ensuring that the cost of the reserve network does not exceed the budget.

While some users may feel that explicitly defining an objective for a conservation problem adds some element of arbitrariness or subjectivity to the decision making process, we remind them that "canned" decision support tools (such as Marxan or Zonation) also have objectives. The key difference here is that instead of choosing between different software programs here the user is explicitly choosing their objective within the single environment.

Please note that failing to specify an objective will return a default error message when solving.

Details

The following objectives can be added to a conservation planning problem:

add_min_set_objective

Set an objective to find the solution that fulfills all the targets and constraints for the smallest cost. This objective is similar to that used in Marxan.

add_max_cover_objective

Set an objective to find the solution that secures as much of each feature as possible without exceeding the budget.

add_max_features_objective

Set an objective to find to find the solution that fulfills as many targets as possible while ensuring that the cost of the solution does not exceed budget and that all constraints are met.

add_max_phylo_objective

Set an objective to find the solution that fulfills as much of a representative sample a phylogenetic tree as possible given a budget. This objective is similar to add_max_features_objective except that emphasis is placed on phylogenetic representation rather than target representation. This objective requires the "ape" R package to be installed.

add_max_utility_objective

Set an objective to find the solution that secures as much of each feature as possible without exceeding the budget.

See Also

constraints, decisions, penalties, portfolios, problem, solvers, targets.

Examples

# NOT RUN {
# load data
data(sim_pu_raster, sim_features, sim_phylogeny)

# create base problem
p <- problem(sim_pu_raster, sim_features) %>%
     add_relative_targets(0.1)

 # create problem with added minimum set objective
p1 <- p %>% add_min_set_objective()

# create problem with added maximum coverage objective
# note that this objective does not use targets
p2 <- p %>% add_max_cover_objective(5000)

# create problem with added maximum feature representation objective
p3 <- p %>% add_max_features_objective(5000)

# create problem with added maximum phylogenetic representation objective
p4 <- p %>% add_max_phylo_objective(5000, sim_phylogeny)

# create problem with added maximum utility objective
# note that this objective does not use targets
p5 <- p %>% add_max_utility_objective(5000)

# }
# NOT RUN {
# solve problems
s <- stack(solve(p1), solve(p2), solve(p3), solve(p4), solve(p5))

# plot solutions
plot(s, axes = FALSE, box = FALSE,
     main = c("minimum set", "maximum coverage", "maximum representation",
              "phylogenetic representation", "maximum utility"))
# }
# NOT RUN {
# }