About

Thomas Magnanti is an Institute Professor and a Professor of Operations Research at the MIT Sloan School of Management. He is an expert in the theory and application of large-scale optimization systems, including communications systems, production planning and scheduling, transportation planning, facility location, logistics, and network design. Magnanti has co-authored two textbooks: 'Applied Mathematical Programming' and 'Network Flows: Theory, Algorithms, and Applications.' He holds a BS in chemical engineering from Syracuse University, as well as MS degrees in statistics and mathematics, and a PhD in operations research from Stanford University. His research and teaching focus on optimization systems and their practical applications in various fields.

Research topics

• Computer Science
• Mathematics
• Mathematical optimization
• Operations research
• Theoretical computer science
• Computer network
• Engineering

Selected publications

• Decision-Dependent Robust Charging Infrastructure Planning for Light-Duty Truck Electrification at Industrial Sites: Scheduling and Abandonment

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Distributionally Robust Games for Data Center Demand Response Coordination based on CPU utilization and Quality of Service

  2025-07-27

  article

  In light of the soaring energy demand of data centers (DC), cloud providers seek effective solutions to reduce power consumption and ease grid stress. Dynamic voltage and frequency scaling (DVFS) could reduce CPU power consumption and provide demand responses. However, its real-time performance depends on uncertain and volatile CPU utilization rates, which highly undermine demand response service quality. This paper first presents a scalable distributionally robust game framework for the aggregator to coordinate DC for the DVFS demand responses. Each DC’s strategy is formulated as a two-stage Wasserstein-metrics distributionally robust optimization (DRO) formulation. To enhance the game scalability, we compare two game formulations, a complementarity model and an equivalent optimization model based on variational inequalities (VI). Using millions of CPU readings from Microsoft virtual machines, games are performed with varying numbers of players. When the game solutions exist, both models achieve the required demand reduction with quality of service (QoS) considerations for each DC. The equivalent optimization model could significantly reduce computation times and allow a 100-player game to be solved on a laptop in one minute.

  PublisherDOI

• Decision-dependent Robust Charging Infrastructure Planning for Light-duty Truck Electrification at Industrial Sites

  ArXiv.org · 2025-10-15

  preprintOpen accessSenior author

  Many industrial sites and digital logistics platforms rely on diesel-powered light-duty trucks to transport workers and small-scale facilities, which results in a significant amount of greenhouse gas emissions (GHGs). To address this, we develop a robust model for planning charging infrastructure to electrify light-duty trucks at industrial sites. The model is formulated as a mixed-integer linear program (MILP) that optimizes the charging infrastructure selection (across multiple charger types and locations) and determines charging schedules for each truck based on the selected infrastructure. Given the strict stop times and schedules at industrial sites, we introduce a scheduling-with-abandonment problem in which trucks forgo charging if their waiting time exceeds a maximum threshold. We further incorporate the impacts of overnight charging and range anxiety on drivers' waiting and abandonment behaviors. To model stochastic, heterogeneous parking durations, we classified trucks using machine learning (ML) methods based on contextual and time-location features. We then constructed decision-dependent, feature-driven robust uncertainty sets in which parking-time variability varies flexibly with drivers' charging choices. These feature-driven sets are applied to two robust optimization formulations with decision-dependent uncertainty (RO-DDU), resulting in distinct outcomes and managerial implications. We conduct a case study at an open-pit mining site to plan charger installations across eight charging zones, serving approximately 200 trucks. By decomposing the problem into a short rolling horizon or using a heuristic approach for the full-year or representative-day dataset, the model achieves an optimality gap of less than 0.1\% under diverse uncertainty scenarios.

  PublisherOA PDFDOI

• Scheduling with Testing of Heterogeneous Jobs

  Management Science · 2023 · 5 citations

  • Computer Science
  • Computer Science
  • Mathematical optimization

  This paper studies a canonical general scheduling model that captures the fundamental trade-off between processing jobs and performing diagnostics (testing). In particular, testing reveals the required processing time and urgency of need-to-schedule jobs to inform future scheduling decisions. The model captures a range of important applications. Prior work focused on special cases (e.g., jobs with independent and identically distributed processing time) to devise optimal policies. In contrast, the current paper studies the most general form of the model and describes two simple heuristics to solve it; adaptive weighted shortest processing time is an adaptive generalization of Smith’s rule that optimally solves several important extensions of previously studied models, whereas index policy optimally solves a closely related stochastic optimization bandit problem. The latter achieves an approximation guarantee that quickly approaches a constant factor that is bounded by two as the number of jobs grows and approaches optimally when the testing time decreases. Extensive numerical experiments suggest that our policies effectively solve the general setting (under 0.1% from optimal on average and under 10% from optimal in rare, worst-case instances). This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4833 .

  PublisherDOI

• Optimization: From Its Inception

  Management Science · 2021-03-19 · 3 citations

  article1st authorCorresponding

  Optimization has been one of the most fundamental and extensive contributions of management science/operations research, with an enormous number of contributions and subfields developed by many researchers and practitioners. When the journal Management Science launched in 1954, little was known about optimization, including some results in nonlinear optimization and the simplex method and duality developed for linear programming. However, linear programming computations were limited to problems with at most 101 linear constraints. Then some early contributions by seminal researchers began to develop foundations for the field. I will review a few of these early contributions, focusing on the traveling sales problem and integer programming, decomposition, and column generation. I will summarize some research and applied contributions since then, including the enormous development of computations. I will focus on linear and integer programs with some material on combinatorial optimization. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.

  PublisherDOI

• Network Design with Routing Requirements

  Springer eBooks · 2020 · 3 citations

  • Computer Science
  • Computer Science
  • Mathematical optimization
  PublisherDOI

• An Adaptive SPT Rule for Scheduling and Testing Heterogeneous Jobs

  SSRN Electronic Journal · 2019-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Scheduling with Testing

  Management Science · 2018-05-30 · 32 citations

  articleOpen access

  We study a new class of scheduling problems that capture common settings in service environments, in which one has to serve a collection of jobs that have a priori uncertain attributes (e.g., processing times and priorities) and the service provider has to decide how to dynamically allocate resources (e.g., people, equipment, and time) between testing (diagnosing) jobs to learn more about their respective uncertain attributes and processing jobs. The former could inform future decisions, but could delay the service time for other jobs, while the latter directly advances the processing of the jobs but requires making decisions under uncertainty. Through novel analysis we obtain surprising structural results of optimal policies that provide operational managerial insights, efficient optimal and near-optimal algorithms, and quantification of the value of testing. We believe that our approach will lead to further research to explore this important practical trade-off. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2973 . This paper was accepted by Yinyu Ye, optimization.

  PublisherOA PDFDOI

• Allocating Students to Multidisciplinary Capstone Projects Using Discrete Optimization

  INFORMS Journal on Applied Analytics · 2018-05-22 · 15 citations

  article1st authorCorresponding

  We discuss an allocation mechanism of capstone projects to senior-year undergraduate students, which the recently established Singapore University of Technology and Design (SUTD) has implemented. A distinguishing feature of these projects is that they are multidisciplinary ; each project must involve students from at least two disciplines. This is an instance of a bipartite many-to-one matching problem with one-sided preferences and with additional lower and upper bounds on the number of students from the disciplines that must be matched to projects. This leads to challenges in applying many existing algorithms. We propose the use of discrete optimization to find an allocation that considers both efficiency and fairness. This provides flexibility in incorporating side constraints, which are often introduced in the final project allocation using inputs from the various stakeholders. Over a three-year period from 2015 to 2017, the average rank of the project allocated to the student is roughly halfway between their top two choices, with around 78 percent of the students assigned to projects in their top-three choices. We discuss practical design and optimization issues that arise in developing such an allocation.

  PublisherDOI

• Allocating Students to Multidisciplinary Capstone Projects Using Discrete Optimization

  DSpace@MIT (Massachusetts Institute of Technology) · 2018-05-01

  articleOpen access1st authorCorresponding

  We discuss an allocation mechanism of capstone projects to senior-year undergraduate students, which the recently established Singapore University of Technology and Design (SUTD) has implemented. A distinguishing feature of these projects is that they are multidisciplinary ; each project must involve students from at least two disciplines. This is an instance of a bipartite many-to-one matching problem with one-sided preferences and with additional lower and upper bounds on the number of students from the disciplines that must be matched to projects. This leads to challenges in applying many existing algorithms.We propose the use of discrete optimization to find an allocation that considers both efficiency and fairness. This provides flexibility in incorporating side constraints, which are often introduced in the final project allocation using inputs from the various stakeholders. Over a three-year period from 2015 to 2017, the average rank of the project allocated to the student is roughly halfway between their top two choices, with around 78 percent of the students assigned to projects in their top-three choices. We discuss practical design and optimization issues that arise in developing such an allocation.

  Publisher

Frequent coauthors

• Prakash Mirchandani

  20 shared

• Anantaram Balakrishnan

  The University of Texas at Austin

  20 shared

• James B. Orlin

  12 shared

• Ravindra K. Ahuja

  10 shared

• Georgia Perakis

  10 shared

• Richard T. Wong

  University of Baltimore

  9 shared

• Retsef Levi

  8 shared

• Karthik Natarajan

  Vinayaka Missions University

  7 shared

Awards & honors

• 2016 Harold W. Kuhn Award