Generate a portfolio of solutions for a conservation planning problem() by finding a pre-specified number of solutions that are closest to optimality (i.e the top solutions).

add_top_portfolio(x, number_solutions)

Arguments

x

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

number_solutions

integer number of solutions required.

Value

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

Details

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.

See also

Examples

# \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 multi-zone 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)

# }