183 citations · 295 across the 13 of their papers we have counts for
16 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,…
Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving
Rui Ai, Hugo De Oliveira Barbalho, Sirui Li +3
A common challenge in real-time operations is deciding whether to re-solve an optimization problem or continue using an existing solution. While modern data platforms may collect i…
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…
Towards Foundation Models for Mixed Integer Linear Programming
Sirui Li, Janardhan Kulkarni, Ishai Menache +2
Mixed Integer Linear Programming (MILP) is essential for modeling complex decision-making problems but faces challenges in computational tractability and requires expert formulatio…
Small Language Models for Application Interactions: A Case Study
Beibin Li, Yi Zhang, Sébastien Bubeck +2
We study the efficacy of Small Language Models (SLMs) in facilitating application usage through natural language interactions. Our focus here is on a particular internal applicatio…
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…
A Deep Learning Perspective on Network Routing
Yarin Perry, Felipe Vieira Frujeri, Chaim Hoch +4
Routing is, arguably, the most fundamental task in computer networking, and the most extensively studied one. A key challenge for routing in real-world environments is the need to…
Truthful Online Scheduling of Cloud Workloads under Uncertainty
Moshe Babaioff, Ronny Lempel, Brendan Lucier +3
Cloud computing customers often submit repeating jobs and computation pipelines on \emph{approximately} regular schedules, with arrival and running times that exhibit variance. Thi…
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…
Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency
Sebastian Perez-Salazar, Ishai Menache, Mohit Singh +1
Cloud computing has motivated renewed interest in resource allocation problems with new consumption models. A common goal is to share a resource, such as CPU or I/O bandwidth, amon…
ERA: A Framework for Economic Resource Allocation for the Cloud
Moshe Babaioff, Yishay Mansour, Noam Nisan +7
Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal alloca…
Truthful Online Scheduling with Commitments
Yossi Azar, Inna Kalp-Shaltiel, Brendan Lucier +4
We study online mechanisms for preemptive scheduling with deadlines, with the goal of maximizing the total value of completed jobs. This problem is fundamental to deadline-aware cl…
Flows and Decompositions of Games: Harmonic and Potential Games
Ozan Candogan, Ishai Menache, Asuman Ozdaglar +1
In this paper we introduce a novel flow representation for finite games in strategic form. This representation allows us to develop a canonical direct sum decomposition of an arbit…