Thomas Magnanti

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

• Network Flows

  2011-09-03 · 2092 citations

  book
  Publisher

• 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 cou…

  PublisherOA PDFDOI

• Maintenance scheduling for modular systems: Modeling and algorithms

  Naval Research Logistics (NRL) · 2014-07-31 · 17 citations

  article

  Abstract We study new models of scheduled maintenance management for modular systems, consisting of multiple components with respective cycle limits. The cycle limit of each component specifies the time interval in which this component must be repaired or replaced. The goal is to compute a feasible maintenance schedule that minimizes the cost associated with component maintenance. Applications of these models arise in Air Force aircraft maintenance as well as in other arenas with required preven…

  PublisherDOI

• 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 student…

  PublisherDOI

• Maintenance and flight scheduling of low observable aircraft

  Naval Research Logistics (NRL) · 2015-01-16 · 7 citations

  article

  Abstract In this article, we focus on relatively new maintenance and operational scheduling challenges that are faced by the United States Air Force concerning low‐observable (LO) or stealth aircraft. The LO capabilities of an aircraft degrade stochastically as it flies, making it difficult to make maintenance scheduling decisions. Maintainers can address these damages, but must decide, which aircraft should be put into maintenance, and for how long. Using data obtained from an active duty Air F…

  PublisherDOI

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

Similar researchers at Massachusetts Institute of Technology

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• David D. Gamarnikh-39