Established originally as a Program at Georgia Tech in 1924, becoming a School in 1945, the School of Industrial and Systems Engineering (ISyE) is the largest academic program of its kind in the United States. With nearly 65 tenure-track faculty, ISyE is able to support not only a broad spectrum of academic concentrations but, importantly, several that have achieved world-class rank. Though the School functions administratively as a single cohesive unit, some of our sub-disciplines or academic specialties are so large and concentrated, they could be legitimately viewed as academic departments in their own right.

ISyE's mission is the creation, assimilation, integration, and dissemination of knowledge involving industrial and systems engineering. In carrying out its educational mission, ISyE seeks to develop a high potential population of full-time, traditional students at the undergraduate and graduate levels as well as those in industry and government who need to acquire new and updated skills for positions of leadership in engineering, academia, and management.

Specifically, ISyE seeks to:

  • Provide selected research and services to industry and government which meet their specific needs;
  • Contribute to the advancement of the ISyE profession through faculty leadership;
  • Facilitate the interface among industries, problems and methodologies; and
  • Enhance the overall reputation of Georgia Tech.

Industrial and Systems Engineering at Georgia Tech will be the world's leader in expanding and communicating engineering knowledge and innovation associated with designing, operating and improving sustainable processes for acquiring, producing, selling and delivering products and services.

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Recent Submissions

  • Some Unconventional Stochastic Programs 

    Liu, Rui Peng (Georgia Institute of Technology, 2022-05-03)
    Stochastic programming is a mathematical optimization model for decision making when the uncertainty is characterized by random events. This thesis is concerned with some stochastic programs that deviate from the conventional ...
  • Decomposition algorithms based on the nonconvex augmented Lagrangian framework 

    Sun, Kaizhao (Georgia Institute of Technology, 2022-05-03)
    Many problems of recent interest arising from engineering and data sciences go beyond the framework of convex optimization and inevitably need to deal with nonconvex structures. The design of efficient optimization algorithms ...

    Kim, Sung Woo (Georgia Institute of Technology, 2022-05-02)
    This dissertation covers mechanism design problems in online logistic platforms. Chapter 2 considers an online order fulfilment problem in E-commerce retail, and we analyze policies based on the worst-case approach. The ...
  • Design and Analysis of Algorithms for Composite Optimization 

    Liang, Jiaming (Georgia Institute of Technology, 2022-04-28)
    In this thesis, we study first-order methods (FOMs) for solving three types of composite optimization problems: convex nonsmooth, convex hybrid, and nonconcex smooth. We revisit three representative methods among FOMs: ...
  • Tractable approximations and algorithmic aspects of optimization under uncertainty 

    Kotsalis, Georgios (Georgia Institute of Technology, 2022-05-10)
    This thesis has two themes. In chapters 1 and 2 we investigate tractable approximations to specific classes of computationally hard problems as they relate to the areas of signal estimation, system identification and ...
  • Simulation optimization in the presence of environmental variables 

    Cakmak, Sait (Georgia Institute of Technology, 2022-04-20)
    Systems whose performance can only be evaluated through expensive numerical or physical simulation are ubiquitous in practice. Numerous works that focus on optimization of such systems have been published over past ...
  • A Unified Lyapunov Framework for Finite-Sample Analysis of Reinforcement Learning Algorithms 

    Chen, Zaiwei (Georgia Institute of Technology, 2022-05-05)
    Reinforcement learning is a framework for solving sequential decision-making problems without requiring the environmental model, and is viewed as a promising approach to achieve artificial intelligence. However, there is ...
  • Statistical Learning and Decision Making for Spatio-Temporal Data 

    Zhu, Shixiang (Georgia Institute of Technology, 2022-04-20)
    Spatio-temporal data modeling and sequential decision analytics are a growing area of research, with an enormous amount of modern spatio-temporal data being consistently collected from the real world. These data include ...
  • Sort Planning for Express Parcel Delivery Systems 

    Khir, Reem (Georgia Institute of Technology, 2022-01-19)
    Parcel logistics services play a vital and growing role in economies worldwide, with customers demanding faster delivery of nearly everything to their homes. To move larger volumes more cost effectively, express carriers ...
  • Managing Operations in Health and Humanitarian Systems Considering Consistency and Uncertainty 

    Guven Kocak, Seyma (Georgia Institute of Technology, 2021-05-01)
    This dissertation focuses on various problems motivated by health and humanitarian systems. When handling the problems, we pay attention to practical aspects of the real applications and consider intangible costs as well ...

    Yildirim, Fatma Melike Melike (Georgia Institute of Technology, 2021-01-26)
    Physical and mental health conditions have an impact on a person’s daily life. If those conditions are not properly treated and managed, it may affect patients’ overall health. This thesis contributes to the decision-making ...
  • Incorporating New Technologies in EEIO Models - Case Study Input Data 

    Pedraza, Cindy Azuero; Thomas, Valerie; Ingwersen, Wesley (Georgia Institute of Technology, 2022)
    This data corresponds to the paper "Incorporating new technologies in EEIO models". These are the input files required to run the new technologies methodology within USEEIO v2 model for the woody-based biofuels case study. ...
  • Solution Techniques for Large-Scale Optimization Problems on the Transmission Grid 

    Johnson, Emma Savannah (Georgia Institute of Technology, 2021-12-14)
    In this thesis, we are interested in solution techniques and primal heuristics for several large-scale optimization problems on the transmission grid. While some of these problems have been studied for a long time, none ...
  • Hyperconnected parcel logistic hubs 

    Buckley, Shannon (Georgia Institute of Technology, 2021-12-14)
    Hyperconnected Parcel Logistic Hubs Shannon Buckley 144 Pages Advised By Dr. Benoit Montreuil This thesis focuses on the design of a new era of hub in the parcel logistics industry. Parcel logistics hubs (hubs) are the ...
  • Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems 

    Peters, Benjamin (Georgia Institute of Technology, 2021-12-14)
    Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault ...
  • Stability, control, and optimization of nonlinear dynamical systems with applications in electric power networks 

    Gholami, Amin (Georgia Institute of Technology, 2021-12-13)
    Electric power systems in recent years have witnessed an increasing adoption of renewable energy sources as well as restructuring of distribution systems into multiple microgrids. These trends, together with an ever-growing ...
  • Decomposition Methods in Column Generation and Data-Driven Stochastic Optimization 

    El Tonbari, Mohamed Ali El Moghazi (Georgia Institute of Technology, 2021-12-13)
    In this thesis, we are focused on tackling large-scale problems arising in two-stage stochastic optimization and the related Dantzig-Wolfe decomposition. We start with a deterministic setting, where we consider linear ...
  • Decision Making in the Presence of Subjective Stochastic Constraints 

    Zhou, Yuwei (Georgia Institute of Technology, 2021-12-09)
    Constrained Ranking and Selection considers optimizing a primary performance measure over a finite set of alternatives subject to constraints on secondary performance measures. When the constraints are stochastic, the ...
  • Faster Conditional Gradient Algorithms for Machine Learning 

    Carderera De Diego, Alejandro Agustin (Georgia Institute of Technology, 2021-12-09)
    In this thesis, we focus on Frank-Wolfe (a.k.a. Conditional Gradient) algorithms, a family of iterative algorithms for convex optimization, that work under the assumption that projections onto the feasible region are ...

    Hurtado Lange, Daniela (Georgia Institute of Technology, 2021-12-07)
    Today’s era of cloud computing and big data is powered by massive data centers. The focus of my dissertation is on resource allocation problems that arise in the operation of these large-scale data centers. Analyzing these ...

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