Generate a portfolio of solutions for a conservation planning
problem()
by finding a prespecified number of solutions that
are closest to optimality (i.e the top solutions).
add_top_portfolio(x, number_solutions)
x 


number_solutions 

Object (i.e. ConservationProblem
) with the portfolio
added to it.
This strategy for generating a portfolio requires problems to
be solved using the Gurobi software suite (i.e. using
add_gurobi_solver()
. Specifically, version 9.0.0 (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.
# \dontrun{
# set seed for reproducibility
set.seed(600)
# load data
data(sim_pu_raster, sim_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)
# print number of solutions found
print(length(s1))
#> [1] 5
# plot solutions
plot(stack(s1), axes = FALSE, box = FALSE)
# create multizone problem with a portfolio for the top 5 solutions
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_top_portfolio(number_solutions = 5) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem and generate portfolio
s2 < solve(p2)
# print number of solutions found
print(length(s2))
#> [1] 5
# plot solutions in portfolio
plot(stack(lapply(s2, category_layer)), main = "solution", axes = FALSE,
box = FALSE)
# }