inst/doc/Repeated_calls.R

In multiscape: Multi-Action, Multi-Objective Spatial Planning

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(multiscape)

## ----base---------------------------------------------------------------------
base <- create_problem(
  pu = data.frame(id = 1:3, cost = 1, area = 10),
  features = data.frame(id = 1L, name = "woodland"),
  dist_features = data.frame(pu = 1:3, feature = 1L, amount = 100)
)

actions_base <- base |>
  add_actions(data.frame(id = c("restore", "control", "fence")), cost = 1)

low_effect <- actions_base |>
  add_effects(data.frame(action = "restore", feature = "woodland", effect = 20))

high_effect <- actions_base |>
  add_effects(data.frame(action = "restore", feature = "woodland", effect = 40))

is.null(actions_base$data$dist_effects)

## ----alternatives-------------------------------------------------------------
objectives_base <- high_effect |>
  add_constraint_targets_absolute(50) |>
  add_objective_min_cost(alias = "cost") |>
  add_objective_max_effect(alias = "benefit")

cbc_problem <- objectives_base |>
  set_solver_cbc(gap_limit = 0, time_limit = 60)

gurobi_problem <- objectives_base |>
  set_solver_gurobi(gap_limit = 0, time_limit = 60)

weighted_problem <- cbc_problem |>
  set_method_weighted_sum(
    aliases = c("cost", "benefit"),
    runs = set_runs_grid(n = 3)
  )

epsilon_problem <- cbc_problem |>
  set_method_epsilon_constraint(
    primary = "cost",
    aliases = c("cost", "benefit"),
    runs = set_runs_manual(data.frame(eps_benefit = c(40, 80)))
  )

augmecon_problem <- cbc_problem |>
  set_method_augmecon(
    primary = "cost",
    aliases = c("cost", "benefit"),
    runs = set_runs_grid(n = 3)
  )

is.null(cbc_problem$data$method)
is.null(objectives_base$data$solve_args)

## ----accumulation-------------------------------------------------------------
constrained <- actions_base |>
  add_action_sets(list(restore_control = c("restore", "control"))) |>
  add_action_sets(list(restore_fence = c("restore", "fence"))) |>
  add_constraint_action_cardinality(3, "max", pu = 1) |>
  add_constraint_action_cardinality(2, "max", pu = 2) |>
  add_constraint_budget(20, "max") |>
  add_constraint_budget(10, "max", include_action_cost = FALSE)

get_action_sets(constrained)
constrained$data$constraints$action_cardinality[, c("count", "sense", "name")]

## ----locks--------------------------------------------------------------------
locked <- actions_base |>
  add_constraint_locked_planning_units(locked_in = 1) |>
  add_constraint_locked_planning_units(locked_out = 3) |>
  add_constraint_locked_actions(locked_out = list(fence = 2)) |>
  add_constraint_locked_actions(locked_out = list(fence = 2))

locked$data$pu[, c("id", "locked_in", "locked_out")]