prioritizr (version 4.1.5)

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).

Usage

add_cuts_portfolio(x, number_solutions = 10L)

Arguments

x

ConservationProblem-class object.

number_solutions

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

Value

ConservationProblem-class object with the portfolio added to it.

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. Please note that version 4.0.1 attempted to use the Gurobi solution pool to speed up the process of obtaining multiple solutions. However, it would sometimes return solutions that were not within the specified optimality gap. To address this, all solution pool methods are provided by the add_pool_portfolio function.

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

portfolios.

Examples

# NOT RUN {
# 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 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)

# }
# 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)
# }
# NOT RUN {
# build multi-zone conservation problem with cuts 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_cuts_portfolio(10) %>%
      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)
# }