Project problem class

Description

This class is used to represent project prioritization problems. A project prioritization problem has actions, projects, and features. Features are the biological entities that need to be conserved (e.g. species, populations, ecosystems). Actions are real-world management actions that can be implemented for conservation purposes (e.g. habitat restoration, monitoring, pest eradication). Each action should have a known cost, and this usually means that each action should have a defined spatial extent and time period (though this is not necessary). Conservation projects are groups of management actions (they can also comprise a singular action too), and each project is associated with a probability of success if all of its associated actions are funded. To determine which projects should be funded, each project is associated with an probability of persistence for the features that they benefit. These values should indicate the probability that each feature will persist if only that project funded and not the additional benefit relative to the baseline project. Missing (NA) values should be used to indicate which projects do not enhance the probability of certain features.

Given these data, a project prioritization problem involves making a decision about which actions should be funded or not---and in turn, which projects should be funded or not---to maximize or minimize a specific objective whilst meeting specific constraints. The objective for a project prioritization problem will always pertain to the probability that features are expected to persist. For example, an objective for a project prioritization problem could be to maximize the maximize the total amount of species that are expected to persist, or minimize the total cost of the funded actions subject to constraints which ensure that each feature meets a target level of persistence. The constraints in a project prioritization problem can be used to specify additional requirements (e.g. certain actions must be funded). Finally, a project prioritization problem---unlike an optimization problem---also requires a method to solve the problem. This class represents a planning problem, to actually build and then solve a planning problem, use the problem() function. Only experts should use this class directly.

Fields

$data: list object containing data.
$objective: Objective object used to represent how the targets relate to the solution.
$decisions: Decision object used to represent the type of decision made on planning units.
$targets: Target object used to represent representation targets for features.
$weights: Weight object used to represent feature weights.
$constraints: Collection object used to represent additional constraints that the problem is subject to.
$solver: Solver object used to solve the problem.

Usage

x$print()

x$show()

x$repr()

x$get_data(name)

x$set_data(name, value)

number_of_actions()

number_of_projects()

number_of_features()

action_names()

project_names()

feature_names()

feature_weights()

feature_phylogeny()

action_costs()

project_costs()

project_success_probabilities()

pf_matrix()

epf_matrix()

pa_matrix()

x$add_objective(obj)

x$add_decisions(dec)

x$add_constraint(con)

x$add_solver(sol)

x$add_targets(targ)

x$add_weights(wt)

x$get_constraint_parameter(id)

x$set_constraint_parameter(id, value)

x$render_constraint_parameter(id)

x$render_all_constraint_parameters()

x$get_objective_parameter(id)

x$set_objective_parameter(id, value)

x$render_objective_parameter(id)

x$render_all_objective_parameters()

x$get_solver_parameter(id)

x$set_solver_parameter(id, value)

x$render_solver_parameter(id)

x$render_all_solver_parameters()

Arguments

name: character name for object.
value: an object.
obj: Objective object.
wt: Weight object.
dec: Decision object.
con: Constraint object.
sol: Solver object.
targ: Target object.
wt: Weight object.
id: Id object that refers to a specific parameter.
value: object that the parameter value should become.

Details

print: print the object.
show: show the object.
repr: return character representation of the object.
get_data: return an object stored in the data field with the corresponding name. If the object is not present in the data field, a waiver object is returned.
set_data: store an object stored in the data field with the corresponding name. If an object with that name already exists then the object is overwritten.
number_of_actions: integer number of actions.
number_of_projects: integer number of projects.
number_of_features: integer number of features.
action_names: character names of actions in the problem.
project_names: character names of projects in the problem.
feature_names: character names of features in the problem.
feature_weights: character feature weights.
feature_phylogeny: ape::phylo() phylogenetic tree object.
action_costs: numeric costs for each action.
project_costs: numeric costs for each project.
project_success_probabilities: numeric probability that each project will succeed.
pf_matrix: Matrix::dgCMatrix object denoting the enhanced probability that features will persist if different projects are funded.
epf_matrix: Matrix::dgCMatrix object denoting the enhanced probability that features is expected to persist if different projects are funded. This is calculated as the pf_matrix multiplied by the project success probabilities.
pa_matrix: Matrix::dgCMatrix object indicating which actions are associated with which projects.
feature_targets: tibble::tibble() with feature targets.
add_objective: return a new ProjectProblem with the objective added to it.
add_decisions: return a new ProjectProblem object with the decision added to it.
add_solver: return a new ProjectProblem object with the solver added to it.
add_constraint: return a new ProjectProblem object with the constraint added to it.
add_targets: return a copy with the targets added to the problem.
get_constraint_parameter: get the value of a parameter (specified by argument id) used in one of the constraints in the object.
set_constraint_parameter: set the value of a parameter (specified by argument id) used in one of the constraints in the object to value.
render_constraint_parameter: generate a shiny widget to modify the value of a parameter (specified by argument id).
render_all_constraint_parameters: generate a shiny div containing all the parameters' widgets.
get_objective_parameter: get the value of a parameter (specified by argument id) used in the object's objective.
set_objective_parameter: set the value of a parameter (specified by argument id) used in the object's objective to value.
render_objective_parameter: generate a shiny widget to modify the value of a parameter (specified by argument id).
render_all_objective_parameters: generate a shiny div containing all the parameters' widgets.
get_weight_parameter: get the value of a parameter (specified by argument id) used in the object's weights.
set_weight_parameter: set the value of a parameter (specified by argument id) used in the object's weights to value.
render_weight_parameter: generate a shiny widget to modify the value of a parameter (specified by argument id).
render_all_weight_parameters: generate a shiny div containing all the parameters' widgets.
get_solver_parameter: get the value of a parameter (specified by argument id) used in the object's solver.
set_solver_parameter: set the value of a parameter (specified by argument id) used in the object's solver to value.
render_solver_parameter: generate a shiny widget to modify the value of a parameter (specified by argument id).
render_all_solver_parameters: generate a shiny div containing all the parameters' widgets.