Conservation planners often desire a portfolio of solutions to present to decision makers. This is because conservation planners often do not have access to "perfect" information, such as cost data that accurately reflects stakeholder preferences, and so having multiple near-optimal solutions can be a useful.


All methods for generating portfolios will return solutions that are within the specified optimality gap.

The following portfolios can be added to a conservation planning problem:


Generate a single solution.


Generate a portfolio of distinct solutions within a pre-specified optimality gap using Bender's cuts.


Generate a portfolio of solutions by extracting all the feasible solutions discovered during the optimization process.


Generate a portfolio of solutions by randomly reordering the data prior to attempting to solve the problem.

See also


# load data data(sim_pu_raster, sim_features) # create problem p <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_binary_decisions() %>% add_default_solver(gap = 0.02, verbose = FALSE) # create problem with cuts portfolio p1 <- p %>% add_cuts_portfolio(4) # create problem with shuffle portfolio p2 <- p %>% add_shuffle_portfolio(4)
# create problem with pool portfolio p3 <- p %>% add_pool_portfolio() # solve problems and create solution portfolios s <- list(solve(p1), solve(p2), solve(p3)) # plot solutions from cuts portfolio plot(stack(s[[1]]), axes = FALSE, box = FALSE)
# plot solutions from shuffle portfolio plot(stack(s[[2]]), axes = FALSE, box = FALSE)
# plot solutions from pool portfolio plot(stack(s[[3]]), axes = FALSE, box = FALSE)