prioritizr: add_shuffle_portfolio – R documentation

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add_shuffle_portfolio

Add a shuffle portfolio

Description

Generate a portfolio of solutions for a conservation planning problem() by randomly reordering the data prior to solving the problem. This is recommended as a replacement for add_top_portfolio() when the Gurobi software is not available.

Usage

add_shuffle_portfolio(
  x,
  number_solutions = 10L,
  threads = 1L,
  remove_duplicates = TRUE
)

Arguments

`x`	`problem()` (i.e. `ConservationProblem`) object.
`number_solutions`	`integer` number of attempts to generate different solutions. Defaults to 10.
`threads`	`integer` number of threads to use for the generating the solution portfolio. Defaults to 1.
`remove_duplicates`	`logical` should duplicate solutions be removed? Defaults to `TRUE`.

Details

This strategy for generating a portfolio of solutions often results in different solutions, depending on optimality gap, but may return duplicate solutions. In general, this strategy is most effective when problems are quick to solve and multiple threads are available for solving each problem separately.

Value

Object (i.e. ConservationProblem) with the portfolio added to it.

Examples

# set seed for reproducibility
set.seed(500)

# load data
data(sim_pu_raster, sim_features, sim_pu_zones_stack, sim_features_zones)

# create minimal problem with shuffle portfolio
p1 <- problem(sim_pu_raster, sim_features) %>%
      add_min_set_objective() %>%
      add_relative_targets(0.2) %>%
      add_shuffle_portfolio(10, remove_duplicates = FALSE) %>%
      add_default_solver(gap = 0.2, verbose = FALSE)

## Not run: 
# solve problem and generate 10 solutions within 20% of optimality
s1 <- solve(p1)

# plot solutions in portfolio
plot(stack(s1), axes = FALSE, box = FALSE)

## End(Not run)
# build multi-zone conservation problem with shuffle portfolio
p2 <- problem(sim_pu_zones_stack, sim_features_zones) %>%
      add_min_set_objective() %>%
      add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5,
                                  ncol = 3)) %>%
      add_binary_decisions() %>%
      add_shuffle_portfolio(10, remove_duplicates = FALSE) %>%
      add_default_solver(gap = 0.2, verbose = FALSE)

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

# print solution
str(s2, max.level = 1)

# plot solutions in portfolio
plot(stack(lapply(s2, category_layer)), main = "solution", axes = FALSE,
     box = FALSE)

## End(Not run)

prioritizr

Systematic Conservation Prioritization in R

v7.0.1

GPL-3

Authors

Jeffrey O Hanson [aut] (<https://orcid.org/0000-0002-4716-6134>), Richard Schuster [aut, cre] (<https://orcid.org/0000-0003-3191-7869>), Nina Morrell [aut], Matthew Strimas-Mackey [aut] (<https://orcid.org/0000-0001-8929-7776>), Matthew E Watts [aut], Peter Arcese [aut] (<https://orcid.org/0000-0002-8097-482X>), Joseph Bennett [aut] (<https://orcid.org/0000-0002-3901-9513>), Hugh P Possingham [aut] (<https://orcid.org/0000-0001-7755-996X>)

Initial release