prioritizr (version 5.0.3)

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 = 10L)

Arguments

x

problem() (i.e. '>ConservationProblem) object.

number_solutions

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

Value

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

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

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)
# }