Multi-objective optimization of stochastic experiments
Date:
Many engineering problems in areas such as system design, process optimization, and supply chains involve multiple competing objectives and noisy experimental outcomes. The relationships between design variables, objectives, and constraints are often black-box in nature, making evaluation expensive and forcing engineers to work with limited experimental budgets. To identify high-quality or near-optimal solutions efficiently, these challenges call for machine learning approaches tailored to scarce data, combined with optimization and statistical learning methods, rather than traditional heuristic algorithms.