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.

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.

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.

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.

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

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 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) # \dontrun{ # solve problem and generate 10 solutions within 20 % of optimality s1 <- solve(p1) # plot solutions in portfolio plot(stack(s1), axes = FALSE, box = FALSE)
# } # 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) # \dontrun{ # solve the problem s2 <- solve(p2) # print solution str(s2, max.level = 1)
#> List of 10 #> $ solution_1 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_2 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_3 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_4 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_5 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_6 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_7 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_8 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_9 :Formal class 'RasterStack' [package "raster"] with 11 slots #> $ solution_10:Formal class 'RasterStack' [package "raster"] with 11 slots #> - attr(*, "objective")= Named num [1:10] 12615 12312 12381 12404 12173 ... #> ..- attr(*, "names")= chr [1:10] "solution_1" "solution_2" "solution_3" "solution_4" ... #> - attr(*, "status")= Named chr [1:10] "OPTIMAL" "OPTIMAL" "OPTIMAL" "OPTIMAL" ... #> ..- attr(*, "names")= chr [1:10] "solution_1" "solution_2" "solution_3" "solution_4" ... #> - attr(*, "runtime")= Named num [1:10] 0.00679 0.0064 0.00664 0.00664 0.00709 ... #> ..- attr(*, "names")= chr [1:10] "solution_1" "solution_2" "solution_3" "solution_4" ...
# plot solutions in portfolio plot(stack(lapply(s2, category_layer)), main = "solution", axes = FALSE, box = FALSE)
# }