Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty

1. 1Formulate a two-stage robust optimization problem with discrete uncertainty
2. 2Apply PRISE to reduce the number of scenarios
3. 3Solve the reduced optimization problem using standard solvers
4. 4Evaluate the quality of the solution obtained
5. 5Refine the scenario reduction process based on the results

Data scientists and operations researchers on a team can benefit from this method to improve the efficiency of their optimization models, while software engineers can implement the PRISE algorithm to integrate it with existing optimization tools

💡 PRISE considers the feasible region and recourse structure to select a representative subset of scenarios, leading to better solution quality and tractability

📈 Improve optimization efficiency with PRISE, a new scenario reduction method for two-stage robust optimization!