News & EventsDepartment Events
Events
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Nov12
EVENT DETAILS
lessAbstract:
This work addresses the computational limitations of the Sample Average Approximation (SAA) method in multistage stochastic programming under Markov-dependent data processes. While SAA is effective for static and two-stage stochastic optimization problems, it becomes computationally prohibitive in multistage settings as the number of samples required to obtain a reasonably accurate solution grows exponentially in the time horizon $T$—a phenomenon known as the curse of dimensionality. To overcome this challenge, we propose a novel data-driven approach: the Markov Recombining Scenario Tree (MRST) method, combined with Stochastic Dual Dynamic Programming (SDDP) as a solution framework. Our analysis shows that MRST achieves polynomial sample complexity in $T$, offering an efficient data-driven alternative to SAA. Extensive numerical experiments further validate the effectiveness of MRST, showcasing its potential to mitigate the curse of dimensionality in multistage stochastic programming.
Bio:
Grani A. Hanasusanto is an Associate Professor in Industrial & Enterprise Systems Engineering at the University of Illinois Urbana-Champaign (UIUC). Previously, he was an Assistant Professor at The University of Texas at Austin and a Postdoctoral Scholar at École Polytechnique Fédérale de Lausanne. He holds a PhD in Operations Research from Imperial College London and an MSc in Financial Engineering from the National University of Singapore. Grani’s research focuses on developing tractable solution approaches for decision-making under uncertainty, with applications in operations management, energy systems, finance, machine learning, and data analytics. His work has been published in leading journals, including Operations Research, Mathematical Programming, SIAM Journal on Optimization, Manufacturing & Service Operations Management, Stochastic Systems, and IEEE Transactions on Power Systems. Grani received the NSF CAREER Award in 2018 and was named a Walker Scholar by the UT Walker Department of Mechanical Engineering, recognizing his contributions to research, teaching, and service. He has served as an INFORMS DEI Ambassador and is currently on the INFORMS DEI Community committee as well as the editorial board of Operations Research as an Associate Editor.
TIME Tuesday, November 12, 2024 at 11:00 AM - 12:00 PM
LOCATION A230, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Nov19
EVENT DETAILS
lessTalk abstract: Benders decomposition is a mathematical decomposition technique designed to solve large-scale linear and mixed-integer programs. Since its introduction in 1962, the approach has been successfully applied to a wide variety of problems arising in supply chain management, transportation, telecommunications, and energy management. Despite its success, however, it has long been overshadowed by dual decomposition methods such as Lagrangian relaxation and Dantzig-Wolfe decomposition. Over the last two decades, one has witnessed a renewed interest in Benders decomposition with the introduction of several novel ideas to improve performance. The purpose of this talk is to give an overview of the main acceleration techniques by focusing on two families of problems where Benders decomposition has proven especially effective: facility location problems and network design problems. After briefly explaining the general methodology and practical enhancements, we will present examples of successful applications to set covering problems and fixed-charge network design problems. In each case, we will focus on strategies for generating strong cuts efficiently, including the application of unified cut generation frameworks and the use of normalization constraints in the dual subproblem.
Bio: Jean-François Cordeau obtained his Ph.D. in Applied Mathematics at École Polytechnique de Montréal in 1999. He is a professor of Operations Management at HEC Montréal, where he also holds the Chair in Logistics and Transportation. He has authored or co-authored more than 175 scientific articles in combinatorial optimization and mathematical programming, focusing primarily on vehicle routing and logistics network design. He has also supervised more than 75 M.Sc. and Ph.D. students. Dr. Cordeau is an Area Editor of Transportation Science and a member of the Editorial Board of Computers & Operations Research. He has worked as a consultant for several Canadian and European organizations in the private and public sectors. He is currently one of the scientific directors of IVADO Labs. He received the Canadian Operational Research Society (CORS) Award of Merit in 2016 and the Pierre-Laurin Award for Research Excellence at HEC Montréal in 2018. In 2023, he and ten of his colleagues won the CORS Practice Prize for their work on maritime vessel routing.
TIME Tuesday, November 19, 2024 at 11:00 AM - 12:00 PM
LOCATION Hive Annex, Ford Motor Company Engineering Design Center map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Dec7
EVENT DETAILS
lessFall classes end
TIME Saturday, December 7, 2024
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec14
EVENT DETAILS
lessThe ceremony will take place on Saturday, December 14 in Pick-Staiger Concert Hall, 50 Arts Circle Drive.
*No tickets required
TIME Saturday, December 14, 2024 at 4:00 PM - 6:00 PM
LOCATION Pick-Staiger Concert Hall map it
CONTACT Andi Joppie andi.joppie@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science