Generate a portfolio of solutions for a conservation planning
problem()
by storing feasible solutions
discovered during the optimization process.
This method is useful for quickly obtaining multiple solutions,
but does not provide any guarantees on the number of solutions, or
the quality of solutions.
add_extra_portfolio(x)
x 


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 8.0.0 (or greater)
of the gurobi package must be installed.
# \dontrun{
# set seed for reproducibility
set.seed(600)
# load data
data(sim_pu_raster, sim_features)
# create minimal problem with a portfolio for extra solutions
p1 < problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.05) %>%
add_extra_portfolio() %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem and generate portfolio
s1 < solve(p1)
# print number of solutions found
print(length(s1))
#> [1] 10
# plot solutions
plot(stack(s1), axes = FALSE, box = FALSE)
# create multizone problem with a portfolio for extra 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_extra_portfolio() %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem and generate portfolio
s2 < solve(p2)
# print number of solutions found
print(length(s2))
#> [1] 10
# plot solutions in portfolio
plot(stack(lapply(s2, category_layer)), main = "solution", axes = FALSE,
box = FALSE)
# }