Package index • multiscape

Build and solve problems

Core workflow to create a planning problem, configure it, and solve it.

create_problem(): Create a planning problem input object
solve(): Solve a planning problem

Actions, effects and profits

Define feasible actions, their effects, and optional profits.

add_actions(): Add management actions to a planning problem
add_effects(): Add action effects to a planning problem
add_benefits(): Add benefits
add_losses(): Add losses
add_profit(): Add profit to a planning problem

Constraints

Define target requirements, area limits, budgets, and locking constraints.

add_constraint_targets_absolute(): Add absolute targets
add_constraint_targets_relative(): Add relative targets
add_constraint_area(): Add area constraint
add_constraint_budget(): Add budget constraint
add_constraint_locked_actions(): Add locked action decisions to a planning problem
add_constraint_locked_planning_units(): Add locked planning units to a problem

Spatial relations

Register boundary and graph-based spatial relations from tables, polygons, or coordinates.

add_spatial_relations(): Add spatial relations
add_spatial_boundary(): Add spatial boundary-length relations
add_spatial_rook(): Add rook adjacency from polygons
add_spatial_queen(): Add queen adjacency from polygons
add_spatial_knn(): Add k-nearest-neighbours spatial relations
add_spatial_distance(): Add distance-threshold spatial relations

Atomic objectives

Register objectives that can be solved individually or combined in multi-objective workflows.

add_objective_min_cost(): Add objective: minimize cost
add_objective_max_benefit(): Add objective: maximize benefit
add_objective_min_loss(): Add objective: minimize loss
add_objective_max_profit(): Add objective: maximize profit
add_objective_max_net_profit(): Add objective: maximize net profit
add_objective_min_fragmentation_planning_units(): Add objective: minimize planning-unit fragmentation
add_objective_min_fragmentation_action(): Add objective: minimize action fragmentation
add_objective_min_intervention_impact(): Add objective: minimize intervention impact

Solver configuration

Store solver and runtime options before calling solve().

set_solver(): Configure solver settings
set_solver_gurobi(): Configure Gurobi solver settings
set_solver_cplex(): Configure CPLEX solver settings
set_solver_cbc(): Configure CPLEX solver settings
set_solver_symphony(): Configure SYMPHONY solver settings

Multi-objective methods

Configure weighted-sum, epsilon-constraint, and AUGMECON workflows.

set_method_weighted_sum(): Set the weighted-sum multi-objective method
set_method_epsilon_constraint(): Set the epsilon-constraint multi-objective method
set_method_augmecon(): Set the AUGMECON multi-objective method

Multi-objective run configuration

Define automatic or manual run designs and execution controls for multi-objective workflows.

set_runs_grid(): Define an automatic multi-objective run grid
set_runs_manual(): Define a manual multi-objective run design
set_runs_control(): Control multi-objective run behavior

Results and extraction

Extract run metadata, objective values, and solution-level results.

get_runs(): Get run-level metadata from a solution set
get_objectives(): Get objective values from a solution set
get_planning_units(): Get planning-unit results from a solution set
get_actions(): Get action results from a solution set
get_features(): Get feature summary from a solution set
get_targets(): Get target achievement summary from a solution set

Solution-set management

Filter, combine, and remove repeated solutions while preserving SolutionSet consistency.

solution_filter(): Filter solutions in a solution set
solution_append(): Append solutions from another solution set
solution_unique(): Keep unique solutions in a solution set

Frontier analysis

Characterize objective ranges and distances to observed reference points.

frontier_extremes(): Find objective-wise extreme solutions
frontier_distances(): Compute distances to observed ideal or nadir points
frontier_knee(): Identify knee solutions on an observed Pareto frontier

Selection analysis

Analyze the recurrence and structural similarity of planning-unit and action assignments.

selection_frequency(): Calculate selection frequency across solutions
selection_similarity(): Calculate structural similarity among solutions

Plotting

Visualize spatial outputs and multi-objective trade-offs.

plot_spatial_planning_units(): Plot selected planning units in space
plot_spatial_actions(): Plot selected actions in space
plot_spatial_features(): Plot spatial feature values from a solution set
plot_tradeoff(): Plot trade-offs from a solution set

Advanced functions

Advanced utilities for inspecting or compiling optimization models.

compile_model(): Compile the optimization model stored in a Problem

Classes

Public classes used across the package.

problem-class Problem: Problem class
solutionset-class SolutionSet: SolutionSet class

Data

Simulated datasets distributed with the package.

sim_dist_features: Simulated feature distribution
sim_features: Simulated features
sim_pu: Simulated planning units
sim_pu_sf: Simulated planning units
load_sim_features_raster(): Example feature raster

Deprecated functions

Deprecated functions kept for backwards compatibility.

add_constraint_locked_pu() obsoleta: Add locked planning units to a problem
add_objective_min_fragmentation_pu() obsoleta: Add objective: minimize planning-unit fragmentation
get_pu() obsoleta: Get planning-unit results from a solution set
plot_spatial_pu() obsoleta: Plot selected planning units in space
run_grid() obsoleta: Define an automatic multi-objective run grid
run_manual() obsoleta: Define a manual multi-objective run design
mo_control() obsoleta: Control multi-objective run behavior