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.
ISE Welcomes Christian Wernz on 11/30/16
Multiscale Decision Theory and its Applications to Healthcare and Beyond
Seminar by Christian Wernz, Assistant Professor
Department of Industrial and Systems Engineering at Virginia Tech
Wednesday, November 30th from 4:00 – 5:00 pm
395 Watts Hall, 2041 College Rd N
Complex socio-technical systems, such as healthcare or energy systems, consist of many interdependent decision makers with different, often conflicting, interests, who interact with each other across multiple system levels and time scales. To model and analyze the decision-making and system design challenges in such environments, I have developed multiscale decision theory (MSDT). MSDT is a modeling approach that combines game theory, Markov decision processes and dependency graphs. In this talk, I will present the foundations of MSDT and how I have applied this methodology to study incentives and payment innovations in the U.S. healthcare system. Specifically, I have analyzed the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs) and its effect on medical technology investments and use. The analysis provides insights for policy makers on how to improve MSSP. Results also inform ACOs, hospitals and physicians on how to optimally respond to this new incentive mechanism. Beyond healthcare, I will discuss applications of MSDT to systems engineering and energy systems.
Dr. Christian Wernz is an Assistant Professor in the Department of Industrial and Systems Engineering at Virginia Tech, where he is the Director of the Multiscale Decision Making (MSDM) laboratory. He received his doctorate in Industrial Engineering and Operations Research from the University of Massachusetts Amherst in 2008. He obtained his bachelor’s and master’s degree in Business Engineering from the Karlsruhe Institute of Technology (KIT), Germany, in 1999 and 2003, respectively. In his research, Dr. Wernz models and analyzes decision-making challenges in organizations and complex systems. His methodological expertise lies in multiscale decision theory, game theory, Markov decision processes, decision analysis and simulation. He has applied these and other operations research methods to study problems in various socio-technical systems with a focus on healthcare. His research has been funded by the National Science Foundation (NSF), the Agency for Healthcare Research & Quality (AHRQ), the Harvey L. Neiman Health Policy Institute, Rolls-Royce, Dell and other industry partners.
Recent ISE Faculty Grant Awards
In the fall of 2016, ISE faculty were awarded a 5-year Training Project Grant from the National Institute for Occupational Safety and Health (NIOSH), the United States federal agency responsible for conducting research and making recommendations for the prevention of work-related injury and illness. Through the grant, they can provide support to graduate students who wish to obtain a master’s degree in industrial engineering, while pursuing their interests in occupational safety and ergonomics. Faculty on the project are Carolyn Sommerich, Steve Lavender, Bill Marras, Mike Rayo, Phil Smith and Dave Woods. You can read more about this master’s level graduate program here.
Another grant was awarded to ISE faculty and researchers in the fall of 2016 from Midmark to assess effects of exam room equipment and layout on workflow and perceptions of patients and clinical staff. Researchers involved include Carolyn Sommerich (ISE), Steve Lavender (ISE), Liz Sanders (Design), and Kevin Evans (SHRS).
Additionally, a 2-year grant was awarded in summer of 2016 from the Ohio BWC Research Grant Program to investigate current methods employed by firefighter-paramedics (FFP) for handling heavier patients and develop improved methods that could reduce the risk of injury to FFP posed by current methods. Researchers on the project are Steve Lavender, Carolyn Sommerich and Bill Marras.
ISE Students Attend SHPE Conference
ISE students, Alejandro Nunez and Manuara Costa, recently returned after attending the Society of Hispanic Professional Engineers (SHPE) national conference on November 2-6, the nation’s largest annual Hispanic STEM conference. The conference is an opportunity for engineering companies to recruit talented students from SHPE membership. Both students who attended were sponsored by the ISE department and are on the E-board of SHPE’s OSU chapter.
Alejandro and Manuara were interested in attending the event to network with companies and other student chapters and obtain a Co-Op/Internship. Alejandro was able to obtain a Co-Op with Delta Airlines through the conference this year and Manuara has attended the conferences since she was a freshman and all of her internships have come from it.
The SHPE mission is to change lives by empowering the Hispanic community to realize its fullest potential and to impact the world through STEM awareness, access, support and development.
You can see photos of the event on the SHPE Facebook page here.
ISE Welcomes Ekundayo Shittu on 11/22/16
Investments in Energy Systems under Uncertainties in Climate Change Policies and Technological Learning
Seminar by Ekundayo Shittu, Assistant Professor
Engineering Management & Systems Engineering, The George Washington University
Tuesday, November 22 from 4:00 – 5:00 pm
210E Baker Systems, 1971 Neil Avenue
When viewed with the lens of developing solution strategies, global climate change is a classic problem of decision making under uncertainty. This presentation highlights the management of responses to climate change specifically as it relates to energy technology investment decisions under two streams of uncertainties. First, we explore the investments of a profit-maximizing firm into a portfolio of energy systems in response to uncertainty in a climate change regulatory policy. We pay close attention to the representation of technological change to investigate how a firm’s optimal investment is influenced by the presence of a set of different technologies, and uncertainty in a carbon tax. We find that the key drivers of the optimal energy technology set are the elasticities of substitution between the different energy sources, and the relative efficacy of research and development (R&D) into energy sources. Second, we extend this analysis by unpacking the role of learning in managing the capacity expansion of existing and emerging energy technologies. Specifically, we address how stochastic learning rates impact capacity investments into a range of electricity generating technologies. Understanding the managerial or strategic response to uncertainty in energy technological learning is particularly important as decision makers face competing energy technology R&D portfolios, dwindling and unstable financial resources, and an imminent climate change policy. With the aid of cost-constrained risk-minimizing inter-temporal optimization model of a reference global energy system, we investigate the effects of uncertainties in technological learning on electricity capacity additions. We find that (1) the willingness to hedge against the inherent risks associated with uncertainty in energy technological learning is positively correlated with the risk premium; (2) near-term or early investments are required in a mix of sustainable energy technological portfolios as a hedge against learning risk. Using data on 1526 energy companies, investor-owned utilities, and government-owned utilities from 1999 to 2010, we combine these two analysis to propose that the effect of external drivers – competitive learning and regulatory policy – on firms’ new technology investments depends on a firm’s existing resource stock. While competing firms’ new technology investments have a stronger effect on incumbents less endowed in the existing technology, regulatory policy pushes incumbents heavily endowed in the existing technology toward environmentally sustainable investments.
This presentation concludes with a peek into future research streams particularly on (1) an innovative agent-based modeling (ABM) framework to evaluate the interconnections among food, energy and water systems, (2) dynamic computable general equilibrium modeling to harness effective insurance decision making strategies in response to natural and climate change induced disasters, and (3) vaccine supply chain logistics in least developed countries.
Keywords: Carbon tax, Energy system; Investments; Learning; Profit-maximization; R&D; Risk; Technological change; Uncertainty.
Professor Ekundayo Shittu conducts basic and applied research that take a systems engineering approach to aid decision making under uncertainty on investments into energy technology portfolios and the economics of climate change response policies. Pivotal to his research is the examination of how key stakeholders deal with climate change risk and uncertainty. He examines ways of integrating formal decision tools and microeconomics to develop climate policies that aid the adoption of emerging energy systems. Current projects include understanding the effects of uncertainty in technological learning on energy capacity additions, investigating how energy firms’ investments are shaped by competitive and regulatory pressures, studying how to adequately value emerging energy systems given the evolution of storage technologies to mitigate intermittency, investigating effective resource use strategies at the nexus of food, water and energy systems, and studying the platforms for an effective and efficient disaster response system. He was a Lead Author on Chapter 2, “Integrated Assessment of Risk and Uncertainty of Climate Change Response Policies,” fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). He is a reviewer for IEEE Transactions on Engineering Management, European Journal of Operations Research, Production and Operations Management journal, Systems Engineering, Energies, Energy Engineering, Vaccines, etc. He holds a doctorate degree in Industrial Engineering and Operations Research from the University of Massachusetts Amherst, a masters degree in Industrial Engineering from the American University of Cairo, and a Bachelors in Electrical Engineering from the University of Ilorin.
ISE Welcomes Ahmed Saif on 11/18/16
Cold Supply Chain Design with Environmental Considerations: A Simulation-Optimization Approach
Seminar by Ahmed Saif, Postdoc
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.
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.
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.
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 https://engineering.osu.edu/news/2016/10/ambitious-student-organization-has-big-ideas-big-data
You can learn more about BDAA at http://bdaaatohiostate.org/.