5 papers
Self-Supervised Tuning of Low-Fidelity Optimization Models via High-Fidelity Feedback
Gonzalo E. Constante-Flores, André H. Quisaguano-Paredes, Asha Ramanujam +2
Low-fidelity (LF) optimization models are widely used in large-scale operations because they can be solved efficiently and reliably, but they often exhibit systematic errors relati…
SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems
Asha Ramanujam, Adam Elyoumi, Hao Chen +5
Most existing safe reinforcement learning (RL) benchmarks focus on robotics and control tasks, offering limited relevance to high-stakes domains that involve structured constraints…
A Tutorial on Multi-time Scale Optimization Models and Algorithms
Asha Ramanujam, Can Li
Systems across different industries consist of interrelated processes and decisions in different time scales including long-time decisions and short-term decisions. To optimize suc…
PAMSO: Parametric Autotuning Multi-time Scale Optimization Algorithm
Asha Ramanujam, Can Li
Optimization models with decision variables in multiple time scales are widely used across various fields such as integrated planning and scheduling. To address scalability challen…
Solution Polishing via Path Relinking for Continuous Black-Box Optimization
Dimitri Papageorgiou, Jan Kronqvist, Asha Ramanujam +3
When faced with a limited budget of function evaluations, state-of-the-art black-box optimization (BBO) solvers struggle to obtain globally, or sometimes even locally, optimal solu…