Multi-Objective Optimization

This guide shows how to run multi-objective optimization with ws3.

Prerequisites

• A loaded ForestModel

• Understanding of multi-objective optimization concepts

Procedure

1. Load the model

from ws3.forest import ForestModel

fm = ForestModel(
    model_name="my_model",
    model_path="path/to/model",
    base_year=2020,
    horizon=10,
    period_length=10
)
fm.import_areas_section()
fm.import_yields_section()
fm.import_actions_section()
fm.import_transitions_section()
fm.initialize_areas()
fm.add_null_action()
fm.reset_actions()

2. Create a Problem and add objectives

from ws3.opt import Problem
from ws3.advanced_modeling import MultiObjectiveOptimizer

problem = Problem(name=\"multi_objective\", sense=1, solver=\"highs\")
# Add your variables and constraints here...

optimizer = MultiObjectiveOptimizer(problem)
optimizer.add_objective(name=\"npv\", weight=0.5, direction=\"maximize\")
optimizer.add_objective(name=\"even_flow\", weight=0.3, direction=\"minimize_deviation\")

3. Solve using weighted sum

result = optimizer.solve_weighted_sum(weights={\"npv\": 0.5, \"even_flow\": 0.3})

4. Inspect results

print(f\"Method: {result['method']}\")
print(f\"Weights: {result['weights']}\")
solution = problem.solution()

Notes

• The ws3.advanced_modeling.MultiObjectiveOptimizer currently supports weighted sum and epsilon-constraint methods.

• For Pareto frontier computation, run multiple optimizations with different weight combinations.