prioritizr (version 8.0.6)

add_cuts_portfolio: Add Bender's cuts portfolio

Description

Generate a portfolio of solutions for a conservation planning problem using Bender's cuts (discussed in Rodrigues et al. 2000). This is recommended as a replacement for add_gap_portfolio() when the Gurobi software is not available.

Usage

add_cuts_portfolio(x, number_solutions = 10)

Value

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

Arguments

x

problem() object.

number_solutions

integer number of attempts to generate different solutions. Defaults to 10.

Notes

In early versions (< 4.0.1), this function was only compatible with Gurobi (i.e., add_gurobi_solver()). To provide functionality with exact algorithm solvers, this function now adds constraints to the problem formulation to generate multiple solutions.

Details

This strategy for generating a portfolio of solutions involves solving the problem multiple times and adding additional constraints to forbid previously obtained solutions. In general, this strategy is most useful when problems take a long time to solve and benefit from having multiple threads allocated for solving an individual problem.

References

Rodrigues AS, Cerdeira OJ, and Gaston KJ (2000) Flexibility, efficiency, and accountability: adapting reserve selection algorithms to more complex conservation problems. Ecography, 23: 565--574.

See Also

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

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

Examples

if (FALSE) {
# set seed for reproducibility
set.seed(500)

# 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 cuts portfolio
p1 <-
  problem(sim_pu_raster, sim_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(0.2) %>%
  add_cuts_portfolio(10) %>%
  add_default_solver(gap = 0.2, verbose = FALSE)

# solve problem and generate 10 solutions within 20% of optimality
s1 <- solve(p1)

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

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

# build multi-zone conservation problem with cuts portfolio
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_binary_decisions() %>%
 add_cuts_portfolio(10) %>%
 add_default_solver(gap = 0.2, verbose = FALSE)

# solve the problem
s2 <- solve(p2)

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

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

# plot solutions in portfolio
plot(s2, main = "solution", axes = FALSE)
}