objectives: Problem objective

In oppr: Optimal Project Prioritization

Problem objective

Description

An objective is used to specify the overall goal of a project prioritization problem(). All project prioritization problems involve minimizing or maximizing some kind of objective. For instance, the decision maker may require a funding scheme that maximizes the total number of species that are expected to persist into the future whilst ensuring that the total cost of the funded actions does not exceed a budget. Alternatively, the planner may require a solution that ensures that each species meets a target level of persistence whilst minimizing the cost of the funded actions. A project prioritization problem() must have a specified objective before it can be solved, and attempting to solve a problem which does not have a specified objective will throw an error.

Details

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

add_max_richness_objective()

Maximize the total number of features that are expected to persist, whilst ensuring that the cost of the solution is within a pre-specified budget (Joseph, Maloney & Possingham 2009).

add_max_targets_met_objective()

Maximize the total number of persistence targets met for the features, whilst ensuring that the cost of the solution is within a pre-specified budget (Chades et al. 2015).

add_max_phylo_div_objective()

Maximize the phylogenetic diversity that is expected to persist into the future, whilst ensuring that the cost of the solution is within a pre-specified budget (Bennett et al. 2014, Faith 2008).

add_min_set_objective()

Minimize the cost of the solution whilst ensuring that all targets are met. This objective is conceptually similar to that used in Marxan (Ball, Possingham & Watts 2009).

References

Ball IR, Possingham HP & Watts M (2009) Marxan and relatives: software for spatial conservation prioritisation. Spatial conservation prioritisation: Quantitative methods and computational tools, 185-195.

Bennett JR, Elliott G, Mellish B, Joseph LN, Tulloch AI, Probert WJ, Di Fonzo MMI, Monks JM, Possingham HP & Maloney R (2014) Balancing phylogenetic diversity and species numbers in conservation prioritization, using a case study of threatened species in New Zealand. Biological Conservation, 174: 47–54.

Chades I, Nicol S, van Leeuwen S, Walters B, Firn J, Reeson A, Martin TG & Carwardine J (2015) Benefits of integrating complementarity into priority threat management. Conservation Biology, 29, 525–536.

Faith DP (2008) Threatened species and the potential loss of phylogenetic diversity: conservation scenarios based on estimated extinction probabilities and phylogenetic risk analysis. Conservation Biology, 22: 1461–1470.

Joseph LN, Maloney RF & Possingham HP (2009) Optimal allocation of resources among threatened species: A project prioritization protocol. Conservation Biology, 23, 328–338.

See Also

constraints, decisions, problem(), solvers, targets, weights.

Examples

# load data
data(sim_projects, sim_features, sim_actions, sim_tree)

# build problem with maximum richness objective and $200 budget
p1 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_max_richness_objective(budget = 200) %>%
     add_binary_decisions()

## Not run: 
# solve problem
s1 <- solve(p1)

# print solution
print(s1)

# plot solution
plot(p1, s1)

## End(Not run)

# build problem with maximum phylogenetic diversity objective and $200 budget
p2 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_max_phylo_div_objective(budget = 200, tree = sim_tree) %>%
     add_binary_decisions()

## Not run: 
# solve problem
s2 <- solve(p2)

# print solution
print(s2)

# plot solution
plot(p2, s2)

## End(Not run)
# build problem with maximum targets met objective, $200 budget, and
# 40% persistence targets
p3 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_max_targets_met_objective(budget = 200) %>%
     add_absolute_targets(0.4) %>%
     add_binary_decisions()

## Not run: 
# solve problem
s3 <- solve(p3)

# print solution
print(s3)

# plot solution
plot(p3, s3)

## End(Not run)

# build problem with minimum set objective, $200 budget, and 40%
# persistence targets
p4 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_min_set_objective() %>%
     add_absolute_targets(0.4) %>%
     add_binary_decisions()

## Not run: 
# solve problem
s4 <- solve(p4)

# print solution
print(s4)

# plot solution
plot(p4, s4)

## End(Not run)

oppr documentation built on Sept. 8, 2022, 5:07 p.m.