add_top_portfolio {prioritizr}

Add a top portfolio

Description

Generate a portfolio of solutions for a conservation planning problem by finding a pre-specified number of solutions that are closest to optimality (i.e, the top solutions).

Usage

add_top_portfolio(x, number_solutions = 10)

Arguments

x

problem() object.

number_solutions

integer number of solutions required. Defaults to 10.

Details

This strategy for generating a portfolio requires problems to be solved using the Gurobi software suite (i.e., using add_gurobi_solver(). Specifically, version 8.0.0 (or greater) of the gurobi package must be installed. Note that the number of solutions returned may be less than the argument to number_solutions, if the total number of feasible solutions is less than the number of solutions requested.

Value

An updated problem() object with the portfolio added to it.

See Also

See portfolios for an overview of all functions for adding a portfolio.

Other portfolios: add_cuts_portfolio(), add_default_portfolio(), add_extra_portfolio(), add_gap_portfolio(), add_shuffle_portfolio()

Examples

## Not run: 
# set seed for reproducibility
set.seed(600)

# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()

# create minimal problem with a portfolio for the top 5 solutions
p1 <-
  problem(sim_pu_raster, sim_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(0.05) %>%
  add_top_portfolio(number_solutions = 5) %>%
  add_default_solver(gap = 0, verbose = FALSE)

# solve problem and generate portfolio
s1 <- solve(p1)

# convert portfolio into a multi-layer raster
s1 <- terra::rast(s1)

# print number of solutions found
print(terra::nlyr(s1))

# plot solutions
plot(s1, axes = FALSE)

# create multi-zone problem with a portfolio for the top 5 solutions
p2 <-
  problem(sim_zones_pu_raster, sim_zones_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5, ncol = 3)) %>%
  add_top_portfolio(number_solutions = 5) %>%
  add_default_solver(gap = 0, verbose = FALSE)

# solve problem and generate portfolio
s2 <- solve(p2)

# convert each solution in the portfolio into a single category layer
s2 <- terra::rast(lapply(s2, category_layer))

# print number of solutions found
print(terra::nlyr(s2))

# plot solutions in portfolio
plot(s2, axes = FALSE)

## End(Not run)