Cancer Treatment Planning using Straight Lines, Circles, and Quadratic Equations
Thursday, May 13, 2021
While sophisticated machine learning, statistical, and analytics tools can help make predictions and decisions, sometimes it is the simplest models that provide insight into a difficult problem. In this keynote talk, Archis will describe how middle school algebra and geometry can help plan radiation treatment for cancer. If you allow him to use ellipses, he will demonstrate that we can do a lot more!
Archis is a Professor and Associate Chair in the Department of Industrial & Systems Engineering at the University of Washington in Seattle, where he currently holds the College of Engineering Endowed Professorship in Healthcare Operations Research. He joined the University of Washington as an Assistant Professor in 2006 after receiving a PhD in Industrial and Operations Engineering from the University of Michigan in 2006, and an MS in Management Science and Engineering from Stanford in 2003. He completed his undergraduate education at the Indian Institute of Technology, Bombay, India, in 2001. Archis is a recipient of the NSF CAREER award, and of the award for Excellence in Teaching Operations Research from the Institute of Industrial Engineers. He has served on the editorial boards of several journals. Archis served as the General Chair of the INFORMS 2019 Annual Meeting, and is currently serving as a Program Co-Chair of the 2021 IISE Annual Conference.
Mauricio G.C. Resende
Operations Research at Amazon
Friday, May 14, 2021
Amazon was recently awarded the 2021 INFORMS Prize for effective integration of advanced analytics and operations research/management sciences (OR/MS) in an organization. Amazon applies OR/MS throughout its business, including for facility location, inventory management, and routing. Transportation plays a key role in giving Amazon’s customers a great experience. Though last-mile delivery is perhaps the most customer-
facing mode of transportation, middle-mile of transportation, middle-mile transportation is just as critical. This presentation will focus on Amazon’s middle-mile transportation research science team, the Middle-Mile Planning, Research and Optimization Sciences (mmPROS) team of Amazon Transportation Services. The mmPROS team is built on three pillars: surface research, air science & tech, and pricing & yield management. The impact of this research on provisioning Amazon’s retail delivery network will be explored in this session.
Mauricio G. C. Resende did his undergraduate training in electrical engineering (systems engineering concentration) at the Pontifical Catholic U. of Rio de Janeiro. His graduate work was in operation research, earning an MSc from Georgia Tech and a PhD from UC Berkeley. Resende is most known for his work with metaheuristics, in particular GRASP and biased random-key genetic algorithms, as well as for his work with interior point methods for linear programming and network flows. He has published over 200 papers on optimization and holds 15 U.S. patents. He has edited four handbooks, including the Handbook of Heuristics, the Handbook of Applied Optimization, and the Handbook of Optimization in Telecommunications, and is coauthor of the book Optimization by GRASP. Prior to joining Amazon Research in 2014 as a Principal Research Scientist in the logistics area, Resende was a Lead Inventive Scientist at the Mathematical Foundations of Computing Department of AT&T Bell Labs and at the Algorithms and Optimization Research Department of AT&T Labs Research from 1988 to 2014. Since 2016, he is also Affiliate Professor of Industrial and Systems Engineering at the University of Washington in Seattle. He is a Permanent Member of the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers University. He is a Fellow of INFORMS and was awarded the 2017 Constantine Caratheodory Prize.
Operations Research Applications in Workforce Planning
This presentation includes the review of workforce planning applications of operations research (OR). Emrah will discuss classifying OR techniques applied in workforce planning and will include underlying mathematical concepts, overview models, and limitations.
Emrah Cimren is a Sr. Research Scientist at Amazon currently leading a research science team responsible for candidate segmentation and candidate-job matching. Prior to amazon, he was managing a network optimization team in Starbucks focusing on supporting supply chain strategy and operations. He works within operations research, machine learning, and big data domain providing direction and technical expertise in the areas of workforce optimization, supply chain optimization, retail performance optimization, forecasting, and pricing and markdown optimization, and pay optimization. He has published journal papers and conference proceedings in the area of combinatorial optimization, network modeling, scheduling, simulation, and system dynamics. He referees academic work for several journals and organizes and chairs scheduling, simulation, and sustainability sessions in conferences. Emrah holds a BS degree in Industrial Engineering from Istanbul Technical University in Turkey, an MS degree in Industrial Engineering from Sabanci University in Turkey, and a PhD degree in Operations Research from The Ohio State University.
Applying Geospatial Analytics to Optimize Last Mile Networks
Discussion on the importance of geospatial data and techniques, and present a real world OR applications for last mile delivery optimization.
Dipal Gupta is a Principal Research Scientist at Amazon in the Amazon Logistics organization. She earned her M.S. in Operations Research from Georgia Tech and B.S.E. in Industrial and Operations Engineering from the University of Michigan Ann Arbor. Her research is focused on last mile logistics, optimization, graph theory and geospatial analytics.
Jin Ye is Senior Research Scientist at Amazon in the Amazon Logistics organization. She received her Ph. D in Engineering Physics from Tsinghua University, and M.S. in Industrial & Systems Engineering from the University of Washington in Seattle. Her research is focused on logistics optimization, machine learning and management science.
Queueing Models and Applications
This presentation will address basic theory and algorithms of multi-layer Markov modulated fluid flow (MMFF) processes and their queueing applications. For multi-layer MMFF processes, we review the existing theory and related algorithms. For queueing applications, we show how an MMFF process can be introduced for queueing models and how results for the MMFF process can be used to find queueing quantities. Our queueing examples are the MAP/PH/K queue with customer abandonment and a double-sided queue. We develop algorithms for computing queueing quantities related to customer abandonment, waiting times, and queue lengths for those queueing models.
Dr. He received BSc degree from the University of Science and Technology of China (Mathematics) in 1984, his first Ph.D from the Institute of Applied Mathematics, Chinese Academy of Sciences (Operations Research) in 1989, and his second Ph.D from the University of Waterloo (Management Sciences) in 1996. His research interests are in the areas of Operations Research, Management Sciences, Applied Probability, and Matrix analytic methods. He teaches courses in operations research, industrial engineering, and management science. He is a member of the Institute for Operations Research and the Management Sciences (INFORMS), a member of the Canadian Operational Research Society (CORS), and a member of the Statistical Society of Canada.
Using Statistical Results from Validating 2015-2019 MVP Judging Rubrics to Find Out Future Core Player for NBA Teams
If a team remains in reconstruction period, how could they bring up the core of the team? Our group developed MVP judging rubrics by forming uniform, weighted, power modes. After finding the development mode, all aspects of strengthening potential players will have the capacity to become the tactics initiator or even next MVP.
Chi-Feng Ho came from Taiwan three years ago and right now lives in Palo Alto, CA. He is black belt in karate and certified by the world organization. And also he played for the golf school varsity team. He presented a few oral in the IEOM, JMP Europe conference and already published abstract in ASA, IEOM society.
Multivariate Statistical Modeling of Stock Investment during COVID-19 Outbreak
This presentation will include details on research using many statistical models to determine the best buying, selling and exchanging strategy. In this study, we’ll explain how the cash-stock balance ratio was used to manage investment parts and a systematical triggering system was established to help buy in more stocks.
Chi-Hong Ho is a junior student at Henry M. Gunn High School in the Bay Area. He took part in the STEAMS Training Group. He finished the stock project and won 3rd place of the High School and Middle School STEM Competition, IEOM Harare conference. Right now, he led the STEAMS Training Group members to continue the new team projects.
Home Pricing Problem on the Resale Side of Instant iBuying
iBuying refers to streamlined home purchases and sales in large volumes. An ibuyer acquires a home from an individual owner, renovates quickly, and sells in the open market. Pricing a home in the resale stage involves setting an initial price and changing the price over time until the home is sold, possibly longer than a year. In this session you’ll learn about a model for this problem, discuss estimation of model parameters, and review initial analytical results.
Suleyman is currently an Applied Scientist in the machine learning team at Zillow, which provides analytics support to the Zillow-Offers business. Prior to joining Zillow in 2016, Suleyman was a scientist at GE Global Research, and worked with several GE businesses on solving operations optimization problems. He moved to industry after serving on the faculty of the School of Industrial and Systems Engineering at the University of Oklahoma for more than 10 years, where his research involved logistics and transportation problems.
Performance Evaluation of Two-tier Public Service System
Many service providers share average or real-time delay information with their customers. We consider a two-tier service system where one provider charges a premium or “toll” relative to the other service and where the two service providers may provide different levels of information to their customers. We examine how such differential information sharing affects the performances of the system. “More isn’t always better” can be applied here as well. We find that sharing of real-time information by the toll service provider is beneficial to both service providers, while the sharing by the non-toll or “free” service provider is detrimental not only to the toll service provider but also to the free service provider unless the real-time information in the toll service is available. We explain why the impacts of real-time information are different and how such impacts are reinforced or diminished by the factors in the system.
Ilhyung Kim is a Professor in the Department of Decision Sciences. He holds a PhD in Operations and Technology Management from the Anderson Graduate School of Management at the University of California, Los Angeles. His current research interests include supply chain coordination, manufacturing and service systems design, and statistical learning. His work has been published in the European Journal of Operational Research, International Journal of Production Economics, Computers and Industrial Engineering, International Journal of Production Research, and other outlets. Before joining Western in 2004, Professor Kim had taught at Purdue University and Washington University in St. Louis. At Western, he teaches business statistics, operations management, and predictive analytics.
Mark C. Springer is a Professor in the area of operations management in the Department of Decision Sciences at Western Washington University (WWU). He joined the College of Business and Economics at Western Washington University (WWU) in 1987. He teaches courses on enterprise research planning systems, quality management, and management science. In addition to his teaching at Western, he has developed supply chain management curriculum for use by member schools of the SAP University Alliance. Professor Springer’s current research interests include learning theory, supply chain volatility, e-service quality, and enterprise systems education. His work has been published in the Journal of Operations Management, the European Journal of Operational Research, Computers and Industrial Engineering, the International Journal of Production Economics, and other outlets. He has also been involved with several recent studies analyzing alternative methods for processing commercial traffic through the U.S.-Canadian border.
Session Title TBD
Carrie Parris is an expert in logistics and supply chains, which is the movement of goods, funds, and information; specializing in virtual-physical mismatch problems and technology product management for previously under-invested existing operations. She has spent the last 6+ years at Amazon leading techincal product teams for new scaling efforts in Last Mile, Amazon Go, and, currently, AWS.
As a hybrid product leader-doer, she specializes in bringing bold visions rapidly to life. She creates executable roadmaps, advances technology that scales, and builds (brilliant, happy, inclusive, diverse,) high performing teams. In her time at Amazon, she has developed a reputation as a senior product leader who “sees the future, is right a lot, and knows where to begin” and “gets it done and brightens the room while doing it.”
She is a frequent speaker on strategy, subjects ranging from operations, technology, hiring and developing, inclusion and diversity. She has been a featured speaker at Amazon’s Product Management Conference (ProdCon) for “How to Hire and Develop the Best” and has guest lectured at Georgia Tech’s Scheller School of Business executive MBA program. She is a graduate of the Purdue Industrial Engineering and Georgia Tech MBA programs.
Supply-Shortage and Diffusion Speed in New Product Markets: Brilliance, Blunder, or Serendipity?
Do supply shortages in a new product market benefit a firm? Researchers have shown that product supply shortages can slow down the juggernaut of the diffusion process. Some scholars, on the other hand, have argued that supply scarcity can fuel a sense of exclusivity and increase the product appeal. We focus on understanding the link between the diffusion parameters, supply-shortages, and its impact on the speed-of-diffusion. Counterintuitively, we theorize and find in supply-constrained markets, under certain conditions of social influence of adopters and waiting customers with respect to their w-o-m intensity and reach, it is possible to positively impact the speed-of-diffusion of a new product. Employing a capacity-constrained diffusion model with heterogeneous social influence, we identify conditions under which a diffusion process can be made to speed-up. We implement this model within an Agent-Based Modeling framework and estimate the parameters of our model using a genetic algorithm-based approach. We find that achieving speed-up of the diffusion process is contingent on a delicate balance between the combination of social influence and external influence parameters of a diffusion process, and the level of supply-shortage in a new product market. Archival analysis of the product categories investigated leads us to believe that a manager can leverage the shortage to either speed-up or slowdown the process by achieving this balance. Depending on whether the speed-up or slowdown was intended or not, a manager’s strategy can be deemed as brilliant, a blunder; or purely serendipitous. Based on our findings, we offer manufacturing and marketing strategy insights.
Surya Pathak is a Professor of Operations Management in the UWB School of Business. He received his PhD in interdisciplinary management of technology from Vanderbilt University in 2005. Prior to joining University of Washington, Bothell he has served as a research and technical lead at the Advanced Computing Center for Research and Education developing next generation network based data storage technologies, and as a Senior Research Associate in the Engineering Management Program and Systems and Decision Making group at Vanderbilt University, Nashville, Tennessee.