News Archive

November 7th, 2016

ISE Welcomes Ahmed Saif on 11/18/16

Cold Supply Chain Design with Environmental Considerations: A Simulation-Optimization Approach

Seminar by Ahmed Saif, Postdoc

HEC Montréal

Friday, November 18th, 11:30 am – 12:30 pm

210E Baker Systems, 1971 Neil Avenue

In response to strict regulations and increased environmental awareness, firms are striving to reduce the global warming impact of their operations. Cold supply chains have high levels of greenhouse gas emissions due to the high energy consumption and refrigerant gas leakages. We model the cold supply chain design problem as a mixed-integer concave minimization problem with dual objectives of minimizing the total cost and the global warming impact. Demand is modeled as a general distribution, whereas inventory is managed using a known policy but without explicit formulas for the inventory cost and maximum level functions. We propose a novel hybrid simulation-optimization approach to solve the problem. Lagrangian decomposition is used to compose the model into an integer programming subproblem and sets of single variable concave minimization subproblems that are solved using simulation-optimization. We provide closed-form expressions for the Lagrangian multipliers so that the Lagrangian bound is obtained in a single iteration, alongside a feasible solution. The approach is verified through testing on two realistic cases from different industries, and managerial insights are drawn.

Ahmed Saif is a postdoctoral fellow in the department of Decision Sciences at HEC Montreal. He completed his Ph.D. in Management Sciences from the University of Waterloo in January 2016. He also holds a master’s degree in Engineering Systems & Management and an MBA. Ahmed’s research interests lie in the area of optimization under uncertainty and data analytics. His current research focuses on data-driven robust optimization approaches and their applications in supply chains, particularly integrated, multistage supply chain network design problems. He worked also on warehouse logistics and developed performance improvement approaches that combine data analytics and optimization tools and that utilize the sheer amount of order processing data collected by warehouses. Furthermore, he conducted research in global optimization, healthcare logistics, airline crew scheduling and hybrid power systems. Ahmed has a diverse teaching experience in North American and International postsecondary institutions. Moreover, he worked in engineering and business consulting for about 4 years and had multiple industry internships while pursuing graduate degrees. Ahmed’s research attracted funding from federal and provincial agencies and from the industry.

November 7th, 2016

ISE Welcomes Shuzhong Zhang on 11/16/16

New first-order algorithms for solving block-structured optimization models

Seminar by Shuzhong Zhang

Professor and Head

Department of Industrial and Systems Engineering, University of Minnesota

Wednesday, November 16th, 4:00 – 5:00 pm

395 Watts Hall, 2041 College Rd. N.

In the context of Big Data analytics, first-order algorithms (referring to the solution procedures where no more than gradient information is required in the process) for large-scale structured optimization models have been popular among researchers in the fields including Operations Research and Machine Learning. One important type of first-order algorithms is called the Alternating Direction Method of Multipliers (ADMM). In the past few years, an intensive stream of research effort has been paid to the performance of the ADMM and its many variants designed to accommodate various formulations arising from applications. In this talk, we shall survey several aspects of the afore-mentioned research effort, and present some new results including the convergence of a randomized multi-block ADMM.

Shuzhong Zhang is Professor and founding head of Department of Industrial and System Engineering, University of Minnesota. He received a B.Sc. degree in Applied Mathematics from Fudan University in 1984, and a Ph.D degree in Operations Research from Erasmus University in 1991. He had held faculty positions at Department of Econometrics, University of Groningen (1991-1993), and Econometric Institute, Erasmus University (1993-1999), and Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong (1999-2010). He received the Erasmus University Research Prize in 1999, the CUHK Vice-Chancellor Exemplary Teaching Award in 2001, the SIAM Outstanding Paper Prize in 2003, the IEEE Signal Processing Society Best Paper Award in 2010, and the 2015 SPS Signal Processing Magazine Best Paper Award. Dr. Zhang was an elected Council Member at Large of the MPS (Mathematical Programming Society) (2006-2009), and served as Vice-President of the Operations Research Society of China (ORSC) (2008-2012). He serves on the Editorial Board of several academic journals, including Operations Research, and Management Science.

November 1st, 2016

Ohio State INFORMS Student Chapter Wins Annual Award

The Ohio State University INFORMS Chapter has been selected as a winner of the INFORMS 2016 Student Chapter Annual Award at Magna Cum Laude level. This is a level higher than the group won last year. The Student Chapter Annual Awards recognize their achievements in the past year and serve as a motivation for them to perform well. The award will be presented to them at the Student Awards Ceremony at the upcoming INFORMS Annual Meeting in Nashville, Tennessee on November 14th, 2016.

October 27th, 2016

Student Group, Big Data and Analytics Association (BDAA), Receives Outstanding Innovation and Change Award

The student group Big Data and Analytics Association (BDAA) recently received the Ohio Union’s “Outstanding Innovation and Change Award”. The award recognizes student groups that have made noteworthy progress toward previously articulated goals. In just 3 years, they have increased their active members ten-fold as students and OSU have made efforts to increase analytics on campus.

ISE undergraduates with a special interest in data analytics can pursue the “data analytics and data-driven optimization” track or the “supply chain management and logistics” track. You can read about curriculum requirements on the ISE website. Graduate students can similarly complete M.S. concentrations in these same areas.

You can read more about BDAA’s efforts and recent growth at

You can learn more about BDAA at

October 24th, 2016

ISE Welcomes Aaron Bloom on Nov. 2

Section Manager, Energy Forecasting and Modeling Group

A seminar by Aaron Bloom

Project Manager/Engineer – Electricity Section Supervisor, National Renewable Energy Laboratory

Wednesday, November 2, 2016; 4:00 – 5:00 pm

395 Watts Hall, 2041 College Rd. N.

The Eastern Renewable Generation Integration Study (ERGIS), explores the operational impacts of the wide spread adoption of wind and solar photovoltaics (PV) resources in the Eastern Interconnection. In order to understand some of the economic and reliability challenges of managing hundreds of gigawatts of wind and solar, we developed state of the art tools, data and models for simulating power system operations using hourly unit commitment and 5-minute economic dispatch over an entire year. Using NREL’s high-performance computing capabilities and new methodologies to model operations, we found that the Eastern Interconnection, as simulated with evolutionary change in 2026, could balance the variability and uncertainty of wind and solar PV at a 5-minute level under a variety of conditions. Our simulations achieve instantaneous penetrations that exceed 50% of load while meeting an annual penetration of 30% on an energy basis.  In this work, we analyze potentially challenging system conditions that occur in the simulations and identify opportunities for innovation, regulatory reform, and changes in operating practices that require further analysis to enable the transition to a system with more wind and solar PV. We also released our model and a variety of visualization tools to the public.

Aaron Bloom graduated from the John Glenn College in 2005. After earning his Master’s in Public Administration he moved to Washington, DC to work at the Federal Energy Regulatory Commission where he worked on various rulemakings, market design issues, and NERC standards. In 2012 he joined the National Renewable Energy Laboratory in Golden, CO. There he leads an interdisciplinary team of power systems analysts that study the impact of clean energy technologies on power systems using a range of models and tools. His research is focused on power system planning and electricity market design. He very much enjoys his job and loves riding mountain bikes in Colorado with his family.

October 20th, 2016

Tenure Track Faculty Position at ISE: Complex Large Scale Systems Modeling & Non-Linear Optimization

The Department of Integrated Systems (ISE) at The Ohio State University, one of the nation’s top ten public universities, invites applications for a tenure-track position focusing on Complex Large Scale Systems Modeling and Non-Linear Optimization. All faculty ranks will be considered. ISE offers degrees (BS, MS and Ph.D.) in Industrial and Systems Engineering. Its undergraduate and graduate programs are consistently among the top programs ranked by USNWR, as the Department has exceptional strength in all major areas associated with the discipline. The Department also has an excellent track record for industry engagement and technology transfer.

This position is partially funded by Ohio State’s Discovery Themes Initiative, a significant faculty hiring investment in key thematic areas in which the university can build on its culture of academic collaboration to make a global impact. The successful candidate will be engaged in the Sustainable and Resilient Economy program (, working with a highly collaborative interdisciplinary community of scholars that is taking a holistic approach to understand how production and consumption systems are linked to ecological systems, and how society can achieve more sustainable and resilient development. In particular, integrated assessment modeling and advanced optimization methods are needed to assess economic, environmental and social impacts, benefits, and trade-offs across different spatial and temporal scales.

The successful applicant will be expected to excel at creating and participating in interdisciplinary research teams. This person will establish a strong research program, attract research funding from federal, state and industry resources, supervise graduate student research, and disseminate the results of such research through high quality peer-reviewed publications. Additionally, this faculty member will extend current course offerings in non-linear optimization and the modeling of complex systems at both the undergraduate and graduate levels. A Ph.D. in operations research or mathematical optimization is highly desired.

Interested candidates should electronically submit a complete curriculum vitae, a separate statement of current and future research interests, a statement of teaching philosophy, contact information for five references and three sample publications to the following email address: The search committee will start reviewing applications for this position on Oct. 1, 2016, but it will remain open until filled.

The Ohio State University is committed to establishing a culturally and intellectually diverse environment, encouraging all members of our learning community to reach their full potential. We are responsive to dual-career families and strongly promote work-life balance to support our community members through a suite of institutionalized policies. We are an NSF Advanced Institution and a member of the Ohio/Western Pennsylvania/West Virginia Higher Education Recruitment Consortium.

The Ohio State University is an Equal Opportunity/Affirmative Action Employer. Applications from women and other underrepresented or minority groups are encouraged. Columbus is a thriving metropolitan community, and the University is responsive to the needs of dual career couples.

October 20th, 2016

ISE Welcomes Azadeh Sheidaei on Oct. 28th, 2016

Multiscale Modeling and Characterization of Polymer Nanoreinforced Composites

Seminar by Azadeh Sheidaei, Assistant Professor

Mechanical Engineering, Kettering University; (517) 898-5191

Friday, October 28th, 2016; 3:15-4:15 pm

210E Baker Systems; 1971 Neil Avenue

In recent years, polymer nano-composites (PNCs) have increasingly gained more attention due to their improved mechanical, barrier, thermal, optical, electrical and biodegradable properties in comparison with the conventional micro-composites or pristine polymers. With a modest addition of nanoparticles (usually less than 5wt. %), PNCs offer a wide range of improvements in moduli, strength, heat resistance, biodegradability, as well as decrease in gas permeability and flammability. Although PNCs offer enormous opportunities to design novel material systems, development of an effective numerical modeling approach to predict their properties based on their complex multi-phase and multiscale structure is still at an early stage. In this talk, I will present my research on Multiscale Modeling and Characterization of Polymer Nanoreinforced Composites. I have developed a microstructure inspired material model based on a statistical technique. I have utilized this method to reconstruct the microstructure of Halloysite nanotube (HNT) polypropylene composite, exfoliated Graphene nanoplatelet (xGnP) polymer composite, Fuel cell and Rock. This model was able to successfully predict the mechanical properties of nanocomposites, petrophysical properties such as porosity, permeability, electrical conductivity and mechanical of Rock and thermal and mechanical properties of Fuel cells. This 3D microstructure model was later incorporated in a damage modeling problem in nanocomposite where damage initiation and damage progression have been modeled using cohesive-zone and modified Gurson-Tvergaard-Needleman (GTN) material models. There is a significant difference between the properties of inclusion and the host polymer in polymer nanocomposite, which leads to the damage evolution during deformation due to a huge stress concentration between nanofiller and polymer. The finite element model of progressive debonding in nano-reinforced composite has been proposed based on the cohesive-zone model of the interface. In order to model cohesive-zone, a cohesive zone traction displacement relation is needed. This curve may be obtained either through a fiber pullout experiment or by simulating the test using molecular dynamics. In the case of nano-fillers, conducting fiber pullout test is very difficult and result is often not reproducible. Using our newly developed framework based on molecular dynamics simulation, fiber-matrix pullout test has been conducted in order to obtain traction-displacement curve for cohesive zone model. This damage model was implemented in our 3D model to predict the material response more accurately.

Azadeh Sheidaei received her BSc in Aerospace Engineering from Sharif University of Technology and MSc and PhD degrees in Mechanical Engineering from Michigan State University. Currently, she is an Assistant Professor of Mechanical Engineering at Kettering University in Michigan. Sheidaei’s main research area is “multiscale characterization and computational modeling of advanced material systems such as polymer reinforced composites”. During her graduate study at MSU (2009-2015), she worked at Composite Vehicle Research Center (CVRC) where she worked on numerous research and industrial projects. Those span over the areas of structural integrity of composites, development of constitutive models and computational tools to predict the mechanical behavior of novel materials such as nanocomposites, computational modeling of soft tissue and power sources such as lithium-ion battery and fuel cells. She has published 11 ISI journal articles and 12 technical conference paper which have been cited over 185 times. Sheidaei is a member of the American Society of Composite (ASC), Society for Engineering Education (ASEE), Society of Automotive Engineers (SAE) and Society of Women Engineers (SWE). Sheidaei has received several research and educational grants from NSF, CAAT (Center for Advanced Automotive Technology) and KEEN (The Kern Entrepreneurial Engineering Network) within the last year. Sheidaei is a recipient of the Zonta International Amelia Earhart Fellowship, which is presented to women pursuing a doctoral degree who demonstrate a superior academic record in the field of aerospace-related sciences and engineering. She has also received dissertation competition award while being selected as the outstanding graduate student by the ME Department at Michigan State University.

October 11th, 2016

ISE Welcomes Dr. Jorge Valenzuela on Oct. 19th, 2016

Generating Cheaper and Cleaner Electricity with Flexible Transmission Networks

Seminar by Dr. Jorge Valenzuela

Philpott-WestPoint Stevens Professor and Chair

Industrial and Systems Engineering, Auburn University

Wednesday, October 19, 2016 from 4:00 – 5:00 pm

395 Watts Hall; 2041 College Rd. N.

The generation of electric power presents several challenges. It cannot be conveniently stored. Thus, there should be sufficient production at all times to meet the immediate demand. It uses a mix of energy sources such as nuclear, hydro, coal, oil, gas, wind, solar, and biomass which are usually located at different locations. These sources produce different amounts of emissions and require different investment and operational costs. Both supply and demand are time-dependent and uncertain. The transmission network used to distribute the power to consumption nodes has been usually considered a static structure when economically dispatching power generators. However, multiple studies have reported noticeable savings in power generation costs when dynamically switching transmission lines into/out of service or using dynamic thermal ratings for the transmission lines. In this presentation, I will describe a large-scale mixed integer programming model where the transmission switching and thermal ratings occur at the beginning of a time period (season) and remains unchanged during that period. The objective of the optimization model is to minimize the total energy generation cost over a season subject to loads and N-1 reliability requirements. A decomposition approach is used to solve the optimization problem efficiently. The model is tested on the 14-bus, 39-bus, and 118-bus power systems showing potential cost savings and lower emissions in each case.

Jorge Valenzuela is the Philpott-WestPoint Stevens Professor in the Industrial and Systems Engineering at Auburn University. He holds a B.S. in Electrical Engineering, a M.S. in Mathematics and Statistics, and a M.S. and Ph.D. in Industrial Engineering. He was department chair of the Industrial and Systems Engineering Department during the period 2011-16. He has received the William Walker Superior Teaching Award and the Outstanding Junior Faculty Research Award from the College of Engineering and twice the Outstanding Faculty Teaching Award. Dr. Valenzuela teaches courses in operations research and statistics. His research interests lie in the field of energy modeling, optimization and analysis. His research has been funded by the Argonne National Laboratory, National Science Foundation, and USDA Forest Service. He has over 50 scientific publications. One of his papers received the 2012 best published paper in IEEE Transactions on Power Systems award. He is associate editor of Energy Systems. Dr. Valenzuela has chaired the Energy, Natural Resources and the Environment section of INFORMS. He has served as Publication Chair at the INFORMS and the Winter Simulation conferences as well as served as Chair of the Combined Colloquia at INFORMS.

October 3rd, 2016

ISE Hosts Event for Recent Alumni

The ISE department hosted an event for recent alumni last Friday, September 30th. The event was held at Bar 23 in the Short North and over 80 people were in attendance including some faculty and current students. The event is intended to maintain connections between alumni and the department.

You can see photos of the event at

October 3rd, 2016

Güzin Bayraksan Awarded NSF Grant

ISE Associate Professor, Güzin Bayraksan, was recently awarded a grant by the National Science Foundation (NSF) for data-driven distributionally robust stochastic programming.

Stochastic programming aids in solving difficult problems with many unknown factors. It does so by relying on probability distributions to mathematically represent and predict uncertain events. However, probabilities of possible outcomes are rarely known in real life. Distributionally robust optimization aims to obtain solutions in the presence of such distributional uncertainties. There are a variety of ways to form distributionally robust stochastic programs. However, which type of model to use for which type of data, system, or decision maker is not well understood. This award supports research to have a deeper understanding of this fundamental question and to explore multi-period uncertainties. The project considers long-term water resources management problems that take various sources of input including climate data, hydrological simulations, expert opinions, and so forth. The results, if successful, will yield improved water management, benefitting the U.S. society and economy. The research findings will be incorporated into educational materials on stochastic optimization. The project will therefore contribute to educating students. The water application will be used to demonstrate the societal impact of our field and to attract women to engineering.