ISE Welcomes Dr. Bita Analui on 3/9/17
Overcoming model uncertainty and inaccuracies in power system planning and operations
Seminar by Bita Analui, Ph.D., Postdoctoral Scholar
School of Electrical, Computer, and Energy Engineering; Arizona State University
Thursday, March 9th from 5:30 – 6:30 pm
210E Baker Systems, 1971 Neil Avenue
Planning the operations of power system introduces a number of optimization problems that need to be solved in different time resolutions under various sources of uncertainty. Due to the inherent non-linearities and non-convexities, these problems are often very challenging to solve at large scales. Multistage stochastic optimization is the state of the art tool to address sequential decision-making under uncertainty. This talk focuses on addressing and overcoming inaccuracies that are tied to the practical application of this tool.
First, uncertainty is not only due to a probabilistic model but also data-driven, as there is an intrinsic ambiguity in the choice of a probability model that may result in misleading solutions. To address this problem, we first present a theoretical framework to address model uncertainty (ambiguity) in multistage stochastic optimization problems and the corresponding algorithms to compute the saddle point solution. We present an application of this method to a multistage stochastic economic dispatch incorporating ramping constraint, in which reserve requirements can be represented as distributionally robust scenarios of generation schedules. In the second part of this talk, we address the issue of selecting scenario trees and solving problems dynamically. We show how one can formulate dynamic multistage stochastic unit commitment in which scenarios are selected from a library of scenario trees that is also dynamically updated as new data are observed. Finally, a continuous-time formulation for multistage stochastic unit commitment is proposed to capture the continuous nature of net-load and optimally schedule continuous-time generation and ramping scenarios, further reducing the inaccuracy that is associated with solving a discrete problem rather that the true variational problem.
Bita Analui is a postdoctoral scholar in Electrical, Computer and Energy Engineering School and Postdoc in Computing Best Practice Fellow at Arizona State University. Prior to joining ASU, Bita was a research assistant at Computational Optimization Doctoral College, University of Vienna in Austria, where she received her Ph.D. in Statistics and Operations Research and graduated cum laude in 2014. Her thesis contribution was on the theory and solution algorithms for multistage stochastic optimization problems under model uncertainty. Bita’s research interests include stochastic optimization, data analytics, statistical modelling, scenario generation and designing dynamic decision models under uncertainty for real world class of problems. Much of her recent works span all of these fields in order to provide a new perspective towards conventional problems.
IISE Students Attend Great Lakes Regional Conference
30 Ohio State IISE students attended the 2017 Great Lakes Regional Conference this past weekend at Ohio University. The theme was “Finding the IE in relief” and focused on how to create good by using IE and process improvement skills to benefit the world. The event included a variety of guest speakers, companies, workshops, networking sessions, and an industrial engineering-specific career fair. The students that attended gained skills applicable to all industries and learned more about the numerous career paths an industrial engineer can take after graduation.
OSU also competed in the conference’s simulation competition and brought home first place. The team was composed of Maria Pandolfi, Georgia Lindner, Gunnar Smyth, Brad Eckstein, and Daniel Chang. Ohio State IISE is looking forward to hosting the 2018 IISE Regional Conference next year.
ISE Welcomes Jitamitra Desai on 3/8/17
Data-driven models and algorithms in aviation operations research
Seminar by Jitamitra Desai, Ph.D., Assistant Professor
Manufacturing and Industrial Engineering Cluster
School of Mechanical and Aerospace Engineering
Nanyang Technological University
Wednesday, March 8th, 2017 from 4:00-5:00 pm
144 Baker Systems, 1971 Neil Avenue
In this talk, we present a portfolio of models and algorithms that arise in aviation operations research. Beginning with an introduction to the various operational problems that occur within the Terminal Maneuvering Area (TMA) of an airport, this talk specifically focuses on flight sequencing and scheduling operations within the TMA. While runway sequencing and scheduling has been well studied in the literature, the problem of scheduling flights taking into account the TMA configuration and constraints is a relatively nascent area of research. This problem is proven to be NP-Hard, and detailed data analysis of the problem parameters and their impact on problem complexity is discussed. A 0-1 mixed-integer programming (MIP) formulation is derived, and several model enhancement strategies are presented. Recognizing the computational difficulty in directly employing this MIP model to solve realistic instances, a novel “data-driven splitting algorithm” is prescribed. Detailed algorithmic insights and analyses validate the rationale and strength of the proposed approach, including a combinatorial exposition of the reduction in the search space. Computational results reveal the efficiency of the proposed algorithm in solving large-scale realistic instances in real-time, while achieving the optimal solution in nearly all of the instances.
Dr. Jitamitra (Jita) Desai is currently a faculty member of Operations Research at Nanyang Technological University (NTU) in Singapore. Dr. Desai’s research interests are in the areas of data-driven optimization and big-data analytics, with wide-ranging applications to engineering and management problems. He is currently the lead investigator on several research projects, having garnered over S$10M in research funding from various government and industry agencies in Singapore. He is currently supervising 5 PhD students and 3 postdoctoral scholars. Dr. Desai obtained his Ph.D. from the Industrial and Systems Engineering department at Virginia Tech, and his B.Tech degree is from the Indian Institute of Technology – Madras. Dr. Desai has authored several refereed journal articles and book chapters, and is actively involved in the OR community. He has taught several graduate and undergraduate-level courses in the OR and decision analysis areas, and was awarded the Nanyang Education Award (2014), a university-wide teaching excellence award.
ISE Welcomes Michelle Alvarado on 3/3/2017
Integrated Simulation and Optimization for Decision-Making under Uncertainty with Application to Healthcare
Seminar by Dr. Michelle Alvarado, Visiting Assistant Professor
Department of Industrial and Systems Engineering, Texas A&M University
Friday, March 3rd from 11:30am – 12:30pm
210E Baker Systems, 1971 Neil Avenue
Many real applications require decision-making under uncertainty. These decisions occur at discrete points in time, influence future decisions, and have uncertainties that evolve over time. One such challenging decision-making process is the scheduling of outpatient chemotherapy appointments due to the cyclic nature of the treatment process and uncertainty of the appointment duration, acuity levels, and resource availability. I will present an integrated simulation and optimization methodology for the online scheduling and management of outpatient chemotherapy appointments. A discrete event simulation model of the clinic operations evaluates system performance and a mean-risk stochastic programming model schedules patients and resources. In this novel approach, the simulation and optimization models interact and exchange information leading to solutions that adapt to changes in system data. Data from a Texas oncology clinic was analyzed to populate and verify both the simulation and optimization model. Computational results show that the integrated simulation and optimization approach is preferred to using either methodology alone.
Dr. Michelle Alvarado is a Visiting Assistant Professor in the Department of Industrial and Systems Engineering at Texas A&M University (TAMU). She holds a M. Eng and PhD in Industrial Engineering from TAMU. Her expertise is in simulation and stochastic programming. Dr. Alvarado develops models and algorithms for healthcare decision making under uncertainty. She is currently working on problems involving remote health monitoring diabetes and other chronic diseases. In addition, Dr. Alvarado has ongoing work in the development of a health policy model for reducing hospital readmissions using a penalty-incentive model.
ISE Welcomes Somayeh Sojoudi on February 15, 2017
Data-driven methods for sparse network estimation
Seminar by Somayeh Sojoudi, Assistant Project Scientist
Industrial Engineering and Operations Research, UC Berkeley
Wednesday, February 15th from 4:00 – 5:00 pm
144 Baker Systems, 1971 Neil Avenue
We live in an increasingly data-driven world in which mathematical models are crucial for uncovering properties of systems from measured data. Graphical models are commonly used for capturing the relationships between the parameters of a system using graphs. Graphical models have applications in many areas, such as social sciences, linguistics, neuroscience, biology, and power systems. Learning graphical models is of fundamental importance in machine learning and statistics, and is often challenged by the fact that only a small number of samples are available. Several algorithms (such as Graphical Lasso) have been proposed to address this problem. Despite the popularity of graphical lasso, there is not much known about the properties of this statistical method as an optimization algorithm. In this talk, we will develop new notions of sign-consistent matrices and inverse consistent matrices to obtain key properties of graphical lasso. In particular, we will prove that although the complexity of solving graphical lasso is high, the sparsity pattern of its solution has a simple formula if a sparse graphical model is sought. Besides graphical lasso, there are several other techniques for learning graphical models. However, it is not clear how reliable these methods are and which method should be used for each particular application. To address these problems, we will design a novel framework for generating synthetic data based on stochastic electrical circuits, and use it as a platform to assess the performance of various techniques. We will show that our platform can be used to first find the best algorithm and then identify the best model associated with that algorithm. We will illustrate our results on fMRI data and uncover new properties of brain networks.
Somayeh Sojoudi is an Assistant Project Scientist at the University of California, Berkeley. She received her PhD degree in Control & Dynamical Systems from California Institute of Technology in 2013. She was an Assistant Research Scientist at New York University School of Medicine from 2013 to 2015. She has worked on several interdisciplinary problems in optimization, control theory, machine learning, data analytics, and power systems. Somayeh Sojoudi is an associate editor for the IEEE Transactions on Smart Grid. She is a co-recipient of the 2015 INFORMS Optimization Society Prize for Young Researchers and a co-recipient of the 2016 INFORMS ENRE Energy Best Publication Award. She is a co-author of a best student paper award finalist for the 53rd IEEE Conference on Decision and Control 2014.
ISE Welcomes Vahid Tari on January 27th, 2017
Characterization and Multi-Scale Modeling of Materials
Seminar by Vahid Tari, Postdoctoral Associate
Department of Materials Science and Engineering, Carnegie Mellon University
Friday, January 27th from 11:30 am – 12:30 pm
210E Baker Systems, 1971 Neil Avenue
Integrated Computational Materials Engineering (ICME) is the integration of materials information, captured in computational tools, with engineering product performance analysis and manufacturing-process simulation. The tenets of ICME were captured the Materials Genome Initiative (MGI) to integrate experiments, computation, and theory. With regards to ICME, in the first part of this presentation, I talk about my research experiences in characterization of different structural materials such as steel, and titanium commonly used in automotive and aerospace industry, and describe the role of materials characterization in design and manufacturing. Afterward I explain a three-dimensional image based modeling technique, and discuss the ability of this technique in the grain-scale prediction of stress/strain localization, facilitating damage nucleation during the mechanical performance that is a bottle neck in engineering applications. Lastly, I summarize the future work on characterization and multi-scale modeling of additive manufacturing materials, talk about future path on ICME in grain boundary engineering of materials, and highlight multi-scale modeling of multiphase polycrystalline materials and advanced composite materials.
Vahid Tari is a postdoctoral associate in department of materials science and engineering at Carnegie Mellon University. He obtained his Ph.D. in computational engineering from Mississippi State University. His primary research interests are currently focused on three-dimensional modeling of polycrystalline materials and composites, parallel computing, and materials characterization. He severs as reviewers for publications in some journals such as Materials science and Engineering A and Scripta Materialia.
ISE Welcomes Chen Chen on January 25, 2017
Modelling Power Systems Problems with Polynomial Optimization
Seminar by Chen Chen, Postdoctoral Research Scientist
Industrial Engineering and Operations Research Department, Columbia University
Wednesday, January 25th from 4:00 – 5:00 pm
144 Baker Systems, 1971 Neil Avenue
Power systems must become more resilient to accommodate the growing presence of renewable energy, electric vehicles, and demand-side generation. However, standard models used for planning and operations typically use simplified approximations of power flow that fail to capture the dynamics of a system under duress. This talk concerns the computational challenges and potential rewards of incorporating more accurate models of the physics of power flow with polynomial optimization.
Chen Chen is a postdoc in the IEOR department at Columbia University. His primary research interest is in the design of optimization algorithms for mixed-integer nonconvex programming problems with applications to power systems. Chen obtained his doctorate in IEOR from UC Berkeley and bachelor’s degree in Industrial Engineering from the University of Toronto. His work experience includes internships at the Ontario Power Authority and the Federal Energy Regulatory Commission.
ISE Welcomes Dr. Jinsong Duan on January 11, 2017
Integrated computational engineering for advanced materials and manufacturing innovation
A Seminar by Jinsong Duan, Ph.D.
Chief scientist, General Simulation, LLC
Adjunct professor, University of Dayton
Wednesday, January 11th, 4:00 – 5:00 pm
144 Baker Systems, 1971 Neil Avenue
Integrated Computational Materials Engineering (ICME) is an emerging materials discovery technology that seamlessly integrates materials information obtained in materials modeling, with engineering product performance prediction and manufacturing process simulation. The application of ICME significantly reduces the time and cost of the materials discovery to deployment process while improving quality.
In this talk, I present my research overview of multi-scale modeling of materials and manufacturing, and their applications in aerospace and automotive industry and clean energy. Then I discuss Ni-based super-alloy and optical nanomaterials, and show how multi-scale computational tools can be used to understand processing-structure-property-performance relationship in these materials. Finally, I brief the on-going research in development of computational methodologies for advanced composite materials and process, and highlight some future plans in ICME for Nickel-Aluminum-Bronze alloy and additive manufacturing, high efficiency solar cell, and 3D printing technology.
Dr. Jinsong Duan has developed and applied multi-scale computational methods and high performance computing techniques to study alloys, composite, nanomaterials, additive manufacturing, and their applications in aerospace and automotive industry and clean energy. He has authored and co-authored high impact peer-reviewed publications and has given invited presentations at international conferences and industry and academia. He severs as reviewers for publications in Institute of Physics (UK) and Materials Research Society (USA). Before joining General Simulation, he held positions in Carnegie Mellon University, U.S. Air Force Research Laboratory, and Booz Allen Hamilton.
Guzin Bayraksan Receives Award in Environment and Sustainability
ISE Associate Professor, Guzin Bayraksan, received INFORMS ENRE Best Publication Award in Environment and Sustainability for her work on “Reclaimed Water Network Design under Temporal and Spatial Growth and Demand Uncertainties,” Environmental Modeling & Software, 49, 103–117, 2013 with W. Zhang, G. Chung, P. Pierre-Louis, and K. Lansey. The award is given to the best refereed journal article in the area of environment and sustainability by the INFORMS Section on Energy, Natural Resources, and the Environment (ENRE).
The paper focuses on design of reclaimed water network, considering various uncertainties including city growth. Reclaimed water is treated wastewater that is re-introduced for non-potable water use. Because reclaimed water use saves precious fresh-water resources, the paper helps a community to be sustainable. The paper’s focus is especially very important for sustainability of water-scarce regions. In addition, the paper helps a community by saving money (through cost-effective design) and generating income (through sale of reclaimed water), both of which can be used for other purposes like education. The paper applies the model and solution methodology to a real municipal reclaimed water network (but with hypothetical numbers for publication purposes).
ISE Faculty Awarded Grant by Sustainable and Resilient Economy Program
A Sustainable and Resilient Economy (SRE) Seed Grant has recently been awarded on Energy and Water Infrastructure Planning Under Extreme Events. This collaborative grant is together with Principal Investigator Dr. Guzin Bayraksan, Dr. Antonio Conejo and Dr. Ramteen Sioshansi (ISE) and two colleagues from the Glenn College of Public affairs, Dr. Noah Dormady and Dr. Robert Glenblum. The project will study design and operation of energy and water infrastructures under disasters by using a novel integrated approach to infrastructure planning and utilizing both operations research and behavioral/experimental approaches. The project will support a student and create a proof of concept that will enable federally funded grant proposals.
Güzin Bayraksan is an associate professor in the Integrated Systems Engineering Department at the Ohio State University. Prior to joining OSU, she was a member of the Systems and Industrial Engineering Department and the Graduate Interdisciplinary Program in Applied Mathematics at the University of Arizona. She received her Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin and B.S. in Industrial Engineering from Bosphorus (Bogazici) University in Istanbul, Turkey. Her research interests are in stochastic optimization, particularly Monte Carlo sampling-based and data-driven methods for stochastic programming with applications in water resources management. She is the recipient of 2012 NSF CAREER award, 2012 Five Star Faculty Award (UA), and the 2008 INFORMS best case study award. She served as an elected member (2010-2016) and treasurer (2013-2016) of the Committee on Stochastic Programming (COSP) and is currently serving as past-president of the INFORMS Forum on Women in Operations Research and Management Science (WORMS) and on the editorial board of IIE Transactions.