objectives: Problem objective
In prioritizr/prioritizrutils: Utilities for prioritizr

Description Usage Arguments Details See Also Examples

Conservation planning problems involve minimizing or maximizing an 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. The problem objective specifies the overall goal of the problem.

add_default_objective(x)

add_min_set_objective(x)

add_max_cover_objective(x, budget)

add_max_features_objective(x, budget)

add_max_phylo_objective(x, budget, tree)

`x`	`ConservationProblem-class` object.
`budget`	`numeric` value specifying the maximum expenditure of the prioritization.
`tree`	`phylo` object specifying a phylogenetic tree

All conservation planning problems require an objective in order to be solved. 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. See below for a list of the objectives that can be added to a conservation problem.

add_default_objective: This objective is used when no objective has been explicitly specified by the user. This will result in an error because there is no sensible default.
add_min_set_objective: The objective here is to find the 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: The objective here is to find the solution that secures as much of each feature as possible whilst not exceeding the budget.
add_max_features_objective: The objective here is 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. This objective was inspired by the conservation problem defined in Cabeza et al. XXXX.
add_max_phylo_objective: This objective is similar to add_max_features_objective except that emphasis is placed on preserving as much of a representative sample a phylogenetic tree as possible given a budget. This objective requires the "ape" R package to be installed.

constraints, problem, targets.

# 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)

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)


# solve problems
s <- stack(solve(p1), solve(p2), solve(p3), solve(p4))

# plot solutions
plot(s, main=c("minimum set", "maximum coverage", "maximum representation",
               "phylogenetic representation"))