34 citations · 34 across the 6 of their papers we have counts for
9 papers
TACIT: Optimization Models that Learn from Their Mistakes
Maxime Bouscary, Marco Molinaro, Sirui Li +3
Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge, making them hard to formalize.…
HeurEvo: Agentic Evolution of Hybrid Solver-Augmented Heuristics for Time-Critical Mathematical Optimization
Feijie Wu, Hugo Barbalho, Konstantina Mellou +5
Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discovery for challenging optimization problems. In many practical settings,…
OptiMind: Teaching LLMs to Think Like Optimization Experts
Xinzhi Zhang, Zeyi Chen, Humishka Zope +6
Mathematical programming -- the task of expressing operations and decision-making problems in precise mathematical language -- is fundamental across domains, yet remains a skill-in…
Large Language Models for Supply Chain Decisions
David Simchi-Levi, Konstantina Mellou, Ishai Menache +1
Supply Chain Management requires addressing a variety of complex decision-making challenges, from sourcing strategies to planning and execution. Over the last few decades, advances…
Online Rack Placement in Large-Scale Data Centers: Online Sampling Optimization and Deployment
Saumil Baxi, Kayla Cummings, Alexandre Jacquillat +5
This paper optimizes the configuration of large-scale data centers toward cost-effective, reliable and sustainable cloud supply chains. The problem involves placing incoming racks…
The Power of Migrations in Dynamic Bin Packing
Konstantina Mellou, Marco Molinaro, Rudy Zhou
In the Dynamic Bin Packing problem, items arrive and depart the system in an online manner, and the goal is to maintain a good packing throughout. We consider the objective of…
Large Language Models for Supply Chain Optimization
Beibin Li, Konstantina Mellou, Bo Zhang +2
Supply chain operations traditionally involve a variety of complex decision making problems. Over the last few decades, supply chains greatly benefited from advances in computation…
Online Demand Scheduling with Failovers
Konstantina Mellou, Marco Molinaro, Rudy Zhou
Motivated by cloud computing applications, we study the problem of how to optimally deploy new hardware subject to both power and robustness constraints. To model the situation obs…
Online Virtual Machine Allocation with Predictions
Niv Buchbinder, Yaron Fairstein, Konstantina Mellou +3
The cloud computing industry has grown rapidly over the last decade, and with this growth there is a significant increase in demand for compute resources. Demand is manifested in t…