Events

Lunch Talk: Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models

The University of Toronto Operations Research Group (UTORG) is hosting a lunch talk by Buser Say. The talk is entitled “Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models”.  Lunch and coffee will be provided.  Hope to see you there!

Who: Buser Say, Ph.D. candidate, University of Toronto

 

When: Wednesday, August 01st @ 12:00pm – 1:00pm

Where: MB101

  

Bio-sketch: Buser is a Ph.D. candidate at University of Toronto under the supervision of Professor Scott Sanner, and a member of the Data-Driven Decision Making Lab (D3M). Previously, he has completed my BASc. in Industrial Engineering from University of Toronto (2014) with emphasis on Operations Research, and earned my MASc. from University of Toronto (2016) as a member of the Toronto Intelligent Decision Engineering Laboratory (TIDEL). His main research focus is in the application of Operations Research techniques and Deep Neural Networks to our Data-Driven Automated Hybrid Planning framework.

Abstract:  In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Boolean Satisfiability (FDSAT-Plan) as well as Binary Linear Programming (FD-BLP-Plan). Experimentally, we show the effectiveness of learning complex transition models with BNNs, and test the runtime efficiency of both encodings on the learned factored planning problem. After this initial investigation, we present an incremental constraint generation algorithm based on generalized landmark constraints to improve the planning accuracy of our encodings. Finally, we show how to extend the best performing encoding (FD-BLP-Plan+) beyond goals to handle factored planning problems with rewards.

2018 YinzOR Student Conference and Poster Competition by CMU INFORMS Student Chapter

The CMU INFORMS Student Chapter is excited to announce the second annual YinzOR Student Conference and poster competition, held in Pittsburgh, PA on August 24-25. YinzOR, sponsored by EQT Corporation and the Tepper School of Business, is a single-track conference that brings together students studying OR, OM, MS, IE, CS, or related areas, to facilitate interaction and collaboration with peers. This year we are honored to have speakers from Carnegie Mellon University, University of Pittsburgh, Georgia Tech University, University of Pennsylvania, Lehigh University, University of Michigan Ann Arbor, Google, and EQT Corporation.

In addition to talks, we’re also hosting a poster competition open to all PhD students in related fields. The panel of judges for the poster session will consist of CMU faculty members. The top three poster presenters will win cash prizes: $500 first place, $300 second place, $200 third place.

If you are interested and willing to present a poster, please email us (at informs@andrew.cmu.edu or mneda@andrew.cmu.edu) a one paragraph abstract (no more than 150 words) by July 31. You will be informed of the results by August 7. We provide up to $50 reimbursement for poster printing expenses to the accepted presenters.

More details of the 2018 YinzOR conference can be found on our website, and attendance is open to all PhD students.

Lunch Talk: Simulation model to assess the impact of a centralized scheduling policy for imaging procedures in Ontario

The University of Toronto Operations Research Group (UTORG) is hosting a lunch talk by Christian Silva. The talk is entitled “Simulation model to assess the impact of a centralized scheduling policy for imaging procedures in Ontario”. Lunch and coffee will be provided. Hope to see you there!

Who: Christian Silva, MASc, University of Toronto

 

When: Wednesday, July 18th @ 12:00pm – 1:00pm

Where: MB101

Bio-sketch: Christian is a MASc student in his final year under the supervision of Prof. Michael Carter at the University of Toronto. His area of study is the application of Operations Research in Healthcare. His thesis is a discrete event simulation model to test a new policy to reduce the wait time for imaging procedures. During his master’s degree, he has been involved as TA in multiple courses related to Simulation, Operations Research, and Operations Management, which are the areas of his main interest. Before starting his graduate studies, he worked for 5 years in Logistics and Process Improvement for retail and airlines. Upon graduation, he will join a fintech company as Process Manager.

Abstract:  Due to constrained capacity, wait times for imaging procedures in Ontario hospitals are frequently higher than the provincial target. A new approach that centrally schedules outpatients and assigns them between different locations is expected to be more efficient. Discrete event computer simulation was used to evaluate the impact of this approach by comparing individual hospital models versus a centralized model with imaging demand data from two Ontario hospitals. Results show that the proposed policy can lead to shorter wait times in the system. We also analyzed the different variables that drive better system performance. Final recommendations are given on the real-life application of this policy.

 

Lunch Talk: Multiple Observations and Goodness of Fit in Generalized Inverse Optimization

The University of Toronto Operations Research Group (UTORG) is hosting a lunch talk by Rafiq Mahmood. The talk is entitled “Multiple Observations and Goodness of Fit in Generalized Inverse Optimization”. Lunch and coffee will be provided. Hope to see you there!

When: Wednesday, July 4th @ 12:00pm – 1:00pm

Where: MB101

Bio-sketch: Rafid Mahmood received his B.A.Sc. and M.A.Sc. degrees in Electrical and Computer Engineering from the University of Toronto in 2013 and 2015 respectively. He is pursuing his Ph.D. degree in Mechanical & Industrial Engineering at the University of Toronto. His research interests focus on the intersection of information theory, optimization, and deep learning for applications in multimedia streaming, health care, and sports analytics.
Abstract:  Inverse optimization is the practice of using observed decisions to model a latent optimization problem. This work develops a generalized inverse linear optimization framework for imputing objective function parameters given a data set containing both feasible and infeasible points. We devise assumption-free, exact solution methods to solve the inverse problem; under mild assumptions, we show that these methods can be made more efficient. We extend a goodness-of-fit metric previously introduced for the problem with a single observed decision to this new setting, proving and numerically illustrating several important properties.

Meet with Professor John N. Tsitsiklis

The University of Toronto Operations Research Group (UTORG) is hosting a meet and greet with Professor John N. Tsitsiklis, a renowned operations research expert from MIT. Refreshments will be served. We hope to see you there!

Who: Professor John N. Tsitsiklis

 

When: Wednesday June 27 @ 11:00a.m. – 12:00p.m.

Where: MB101

Bio-Sketch: John N. Tsitsiklis was born in Thessaloniki, Greece, in 1958. He received the B.S. degree in Mathematics (1980), and the B.S. (1980), M.S. (1981), and Ph.D. (1984) degrees in Electrical Engineering, all from the Massachusetts Institute of Technology, Cambridge, Massachusetts, U.S.A.

During the academic year 1983-84, he was an acting assistant professor of Electrical Engineering at Stanford University, Stanford, California. Since 1984, he has been with the department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), where he is currently a Clarence J Lebel Professor of Electrical Engineering.

After serving as acting co-director (Spring 1996 and 1997) and co-associate director (2008-2013), he is now the director of the Laboratory for Information and Decision Systems (LIDS). He has also served as a co-director of the Operations Research Center (ORC) (2002-2005), and as a member of the National Council on Research and Technology in Greece (2005-2007) and the associated Sectoral Research Council on Informatics (2011-2013). Finally, he has served (2013-2016) as the Chair of the Council of the Harokopio University, in Greece.

His research interests are in the fields of systems, optimization, control, and operations research. He is a coauthor of Parallel and Distributed Computation: Numerical Methods (1989, with D. Bertsekas), Neuro-Dynamic Programming (1996, with D. Bertsekas), Introduction to Linear Optimization (1997, with D. Bertsimas), and Introduction to Probability (1st ed. 2002, 2nd. ed. 2008, with D. Bertsekas). He is also a coinventor in seven awarded U.S. patents.

He has been a recipient of an IBM Faculty Development Award (1983), an NSF Presidential Young Investigator Award (1986), an Outstanding Paper Award by the IEEE Control Systems Society (1986), the M.I.T. Edgerton Faculty Achievement Award (1989), the Bodossaki Foundation Prize (1995), the MIT/EECS Louis D. Smullin Award for Teaching Excellence (2015), a co-recipient of two INFORMS Computing Society prizes (1997, 2012), a co-recipient of an ACM Sigmetrics Best Paper Award (2013), and a recipient of the ACM Sigmetrics Achievement Award (2016). He is a Fellow of the IEEE (1999) and of INFORMS (2007). In 2007, he was elected to the National Academy of Engineering. In 2008, he was conferred the title of Doctor honoris causa from the Université catholique de Louvain (Belgium).