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James B. Orlin

James B. Orlin

· E. Pennell Brooks (1917) Professor in Management

Massachusetts Institute of Technology · Operations Research and Statistics

Active 1977–2026

h-index60
Citations23.5k
Papers27410 last 5y
Funding$507k

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

James B. Orlin is the E. Pennell Brooks (1917) Professor in Management and a Professor of Operations Research at the MIT Sloan School of Management. He specializes in network and combinatorial optimization, contributing to the development of improved solution methodologies in airline scheduling, railroad scheduling, logistics, network design, telecommunications, inventory control, and marketing. Orlin has co-authored the award-winning textbook, Network Flows: Theory, Algorithms, and Applications, and holds degrees in mathematics from the University of Pennsylvania and the California Institute of Technology, a MMath from the University of Waterloo, and a PhD in operations research from Stanford University. His work has been recognized with prestigious awards such as the 2020 INFORMS Khachiyan Prize for lifetime achievements in optimization, and the ACM SIGecom Test of Time Award for impactful research in computational social choice. His research and contributions have significantly advanced the fields of network flows and combinatorial optimization.

Research topics

  • Computer Science
  • Algorithm
  • Mathematics
  • Combinatorics
  • Theoretical computer science

Selected publications

  • Fully Polynomial Time Approximation Schemes for Stochastic Dynamic Programs

    SIAM Journal on Discrete Mathematics · 2014-01-01 · 75 citations

    article

    We present a framework for obtaining fully polynomial time approximation schemes (FPTASs) for stochastic univariate dynamic programs with either convex or monotone single-period cost functions. This framework is developed through the establishment of two sets of computational rules, namely, the calculus of $K$-approximation functions and the calculus of $K$-approximation sets. Using our framework, we provide the first FPTASs for several NP-hard problems in various fields of research such as knap…

  • On the power of randomization in network interdiction

    Operations Research Letters · 2015-12-02 · 50 citations

    articleOpen accessSenior author
  • Robust Monotone Submodular Function Maximization

    Lecture notes in computer science · 2016-01-01 · 43 citations

    book-chapterOpen access1st authorCorresponding
  • Robust optimization with incremental recourse

    arXiv (Cornell University) · 2013-12-14 · 19 citations

    preprintOpen accessSenior author

    In this paper, we consider an adaptive approach to address optimization problems with uncertain cost parameters. Here, the decision maker selects an initial decision, observes the realization of the uncertain cost parameters, and then is permitted to modify the initial decision. We treat the uncertainty using the framework of robust optimization in which uncertain parameters lie within a given set. The decision maker optimizes so as to develop the best cost guarantee in terms of the worst-case a…

  • On the complexity of energy storage problems

    Discrete Optimization · 2017-12-20 · 18 citations

    articleOpen accessSenior author

Recent grants

Frequent coauthors

Education

  • Ph.D., Operations Research

    Stanford University

    1981
  • MMath, Combinatorics and Optimization

    University of Waterloo

    1976
  • M.S., Mathematics

    California Institute of Technology

    1976
  • BA, Mathematics

    University of Pennsylvania

    1974

Awards & honors

  • Khachiyan Prize (2020) INFORMS Optimization Society
  • Test of Time Award ACM SIGecom
  • Leonard G. Abraham Prize in Communications (year not specifi…

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