Plenaries and Keynotes
GBCC- General Assembly B, Level 3
The Final Step in the Remarkable Journey of the Isoperimetric Problem: The Completion of Euler’s Approach
Dr. Richard Tapia
In this presentation we give a brief overview of the remarkable life of the impactful isoperimetric problem. We identify three distinct classes of solution approaches that have been used throughout history: the Cartesian coordinate representation approach of Euler, the synthetic geometry approach of Steiner, and the parametric representation approach of Weierstrass. We say that one of our three classes of approaches has been completed when an appropriately short sufficiency proof for the isoperimetric problem has been constructed that belongs to this class of proofs. In a legendary work from 1744, Euler presented his contribution, establishing neither necessity nor sufficiency for this problem. This failure led Steiner in 1838 to propose his approach that gave only necessity and not sufficiency as he believed. The Steiner path was completed by Lawlor in 1998. Euler’s and Steiner’s failures led Weierstrass in 1879 to propose his approach, which did indeed lead to sufficiency but required a somewhat elaborate theory. The Weierstrass approach was completed in 1934 by Littlewood, Hardy, and Polya. The major contribution in this presentation is our completion of Euler’s approach. Our proof uses elementary tools.
Richard Tapia was born in Los Angeles, California to parents who emigrated from Mexico. In 2011 he was awarded the National Medal of Science, the highest honor bestowed by the United States government on scientists and engineers. This award spotlighted the distinguished contributions he has made to the mathematical frontiers of optimization theory and numerical analysis, but it also brought attention to another achievement: his long-time work in inspiring underrepresented minority and female students in science and math.
He is a mathematician in Rice University’s Computational and Applied Mathematics Department and holds the rank of University Professor–the university’s highest academic title awarded to only seven individuals in the university’s history. Among numerous honors, Dr. Tapia was awarded the National Science Board’s Vannevar Bush Award and elected to the National Academy of Engineering; he is the first Hispanic to receive these honors. He holds seven honorary doctorates and has given commencement addresses at six major universities. Two professional conferences have been named in his honor: the Richard Tapia Celebration of Diversity in Computing Conference and the Blackwell-Tapia Mathematics Conference. Tapia served on the National Science Board from 1996 to 2002.
Plenary – OMEGA RHO
GBCC – General Assembly B, Level 3
Sports Scheduling Meets Business Analytics
Carnegie Mellon University in Qatar
Over the last 20 years, the ability of operations research techniques to create practical sports schedules has increased tremendously. Some of this increased ability is due to faster computers and better underlying optimization software. Increased understanding of the unique aspects of sports scheduling optimization has also played a role. This is allowing the integration of predictive and prescriptive analytics resulting in schedules that are not just playable but are also profitable.
Michael Trick is the Dean of Carnegie Mellon University in Qatar and Harry B. and James H. Higgins of Operations Research at the Tepper School of Business, Carnegie Mellon.
He received his doctorate in 1987 and has been at Carnegie Mellon since 1989. His research interests are in computational integer programming, constraint programming, and applications in telecommunications, scheduling, and social choice. He has consulted extensively with organizations such as Major League Baseball, the FCC, and many other firms on scheduling and optimization issues. In 2002, he was President of INFORMS and he is the current President of the International Federation of Operational Research Societies, an umbrella organization of societies for which INFORMS is its US member. He is a Fellow of INFORMS.
Hilton- Ballroom of Americas, Level 2
Optimization: Past, Present, Future
For the vast majority of business applications, optimization means linear and mixed-integer programming. Beginning with Dantzig’s simplex method in 1947, optimization experienced a slow, uneven period of development into the mid 1980s. Then, beginning in the late 1980s, developments ensued that completely transformed optimization and its applications, driven by truly remarkable performance improvements in the underlying solvers. What’s coming next may be even more exciting. Driven by an explosion in available business data, a new broad corporate focus on extracting value from that data, increased computing power, and the continually expanding power of optimization solvers, optimization promises to become an indispensable tool in managing the modern enterprise.
Dr. Robert Bixby has a BS in Industrial Engineering and Operations Research from the University of California, Berkeley (1968), and a PhD in Operations Research from Cornell University (1972). He has held academic positions at the University of Kentucky, Northwestern University, and Rice University, as well as visiting positions at the University of Wisconsin, Cornell University, the Forschungsinstitut für Diskrete Mathematik, Bonn, Universität Augsburg, and the Konrad Zuse Zentrum, Berlin.
He is currently Noah Harding Professor Emeritus of Computational and Applied Mathematics at Rice University, and visiting Professor in the Department of Mathematics at Friedrich-Alexander-Universität Erlangen-Nürnberg. He is also the co-founder (2008) of Gurobi Optimization and served as CEO from 2008-2015.
Dr. Bixby has published over fifty journal articles, and is an acknowledged expert on the computational aspects of linear and integer programming. He has won several awards for his work in optimization, including a Humboldt Senior Scientist award, the Beale-Orchard-Hays Prize of the Mathematical Programming Society, and the INFORMS Impact and Frederick W. Lanchester Prizes. He was Editor-in-Chief Mathematical Programming, Series A, 1989-1994, and Chairman of the Mathematical Programming Society, 2001-2004. In 1997 he was elected to the National Academy of Engineering for his contributions to the theory and practice of optimization. In 2012 he was awarded an honorary doctorate in Mathematics from the University of Waterloo, Canada.
Dr. Bixby has over thirty years of experience in the optimization software business. He co-founded CPLEX Optimization, Inc., in 1987. CPLEX was acquired by ILOG, Inc., in 1997, after which he served on the ILOG Board of Directors, manager of the ILOG CPLEX Development Team, and General Manager of ILOG’s Semiconductor Business Division.
GBCC- General Assembly A, Level 3
INFORMing Process Improvement and Patient Safety in Healthcare
Because of my previous position as Executive Director, Performance Improvement at the University of Texas (UT) M. D. Anderson Cancer Center, the conference committee asked me to share information about and the history behind the Texas Medical Center and MD Anderson. I will also discuss my role at UT as Chancellor’s Fellow for Systems Engineering and how our team was able to establish partnerships between university faculty (in Business and Engineering) and healthcare professionals to spread the use of operations research and management science in healthcare. I will share some exciting accomplishments from those efforts as well as more general applications across the healthcare industry. I hope you will leave this session with a new understanding of the challenges in healthcare and an interest in using Operations Research and Management Science to address some of these challenges.
Dr. Victoria Jordan specializes in systems management, applied statistics and quality improvement. As the Vice President, Quality at Emory Healthcare (Jan, 2017- present), she develops, plans, coordinates, and implements quality improvement efforts across Emory Healthcare. This includes strategic oversight of quality initiatives across Emory’s six hospitals and many primary care and specialty clinics including patient safety and infection prevention, process improvement, regulatory compliance, quality education, and clinical quality analytics.
Dr. Jordan also serves as the Director of Performance Improvement and Analytics for the Kennedy Initiative for Patient-Centered Care where she leads the effort to define and operationalize the vision of expanding EHC performance improvement, quality education, and analytics capabilities using Lean and other approaches to achieve high reliability.
In her previous role as Executive Director of Strategic Management and Systems Engineering at M.D. Anderson Cancer Center (2008-2017), she led Quality/Systems Engineering, Strategic Planning and Management, and Clinical Informatics within the Office of Performance Improvement. Quality/Systems Engineering provided expertise to the organization in process and system improvement by applying quality tools and methodologies that support safety, timeliness, effectiveness, efficiency, equity, and patient-centeredness. Through Clinical Informatics, her area provided accurate and timely process and outcomes data (internal and comparative) to process owners in support of the needs of the EVP of Clinical Operations, the EVP of Research, and Division Heads. In her Strategic Planning and Management role, Dr. Jordan facilitated the development and implementation of the institution’s strategic plan with the Executive Committee. In addition to her position at MD Anderson, Dr. Jordan served as the University of Texas Chancellor’s Health Fellow for Systems Engineering, coordinating and promoting the use of Systems Engineering in health care institutions in collaboration with the Engineering and Business Schools within the University of Texas System.
Dr. Jordan’s research interests include statistical quality control, Six Sigma, process optimization, mathematical simulation of patient flow, and applied statistics. She is the co-author of a McGraw-Hill textbook, Design of Experiments in Quality Engineering, author of several peer reviewed articles, and has served as an Adjunct Professor in Industrial Engineering at the University of Houston and as an Instructor in Industrial and Systems Engineering and Assistant Professor in Statistics at Auburn University. Dr. Jordan also held an appointment as a Research Fellow at the University of Texas Red McCombs College of Business. She serves on the Industry Advisory Board for the University of Houston Industrial Engineering department, the Alumni Council for the Auburn University Industrial and Systems Engineering department, and is the healthcare representative to the national Council of Industrial and Systems Engineers.
Dr. Jordan received her Ph.D. (2006) from Auburn University in Industrial and Systems Engineering with an emphasis in applied statistics and received the Auburn University Distinguished Alumna of the Year Award for Industrial and Systems Engineering in 2017. She holds an M.B.A. from the Ohio State University, an M.S. (1987) in Industrial and Systems Engineering from Auburn University, and a B.S. from the University of Kentucky in Statistics, with minors in Computer Science and Mathematics. Dr. Jordan is a senior member of the American Society for Quality, a member of the American Statistical Association and a Fellow of the Institute of Industrial and Systems Engineers. She is a Six Sigma Master Black Belt (certified by ASQ and BMGi), Certified in Medical Quality, and has over 30 years of experience providing management and statistical consulting in manufacturing, service, and heath care organizations.
GBCC- General Assembly B, Level 3
Systems Approach to Managing Risk in Human Spaceflight Missions
Texas A&M (formerly NASA)
GBCC- Grand Ballroom C, Level 3
IFORS Distinguished Lecture
Biased Random-Key Genetic Algorithms: Components, Evolutionary Dynamics and Applications
Universidade Federal Fluminense
A biased random-key genetic algorithm (BRKGA) is a general search procedure for finding optimal or near-optimal solutions to hard combinatorial optimization problems. It is derived from the random-key genetic algorithm of Bean (1994), differing in the way solutions are combined to produce offspring. BRKGAs have three key features that specialize genetic algorithms. First, a fixed chromosome encoding using a vector of N random keys over the real interval [0, 1), where the value of N depends on the instance of the optimization problem. Second, a well-defined evolutionary process adopting a parameterized uniform crossover to generate offspring and thus evolve the population. Third, the introduction of new chromosomes called mutants in place of the mutation operator usually found in evolutionary algorithms. Such features simplify and standardize the procedure with a set of self-contained tasks from which only one is problem-dependent: that of decoding a chromosome, i.e. using the keys to construct a solution to the underlying optimization problem, from which the objective function value or fitness can be computed. In this talk, we review the basic components of a BRKGA and introduce a framework for quick implementations of BRKGA heuristics. We then illustrate the application of this framework to a few case studies in a network routing, load scheduling and data mining problems. We conclude with a brief review of other domains where BRKGAs have been applied.
Celso C. Ribeiro is a Full Professor at the Department of Computer Science of Universidade Federal Fluminense, Brazil. He obtained his doctorate in Computer Science at the Ecole Nationale Supérieure des Télécommunications, France, in 1983. He chaired the Departments of Electrical Engineering (1983-1987) and Computer Science (1993-1995) of the Catholic University of Rio de Janeiro, Brazil.
He also chaired the Department of Modernization Programs of the Brazilian Ministry Education (2005-2007) and acted as Subsecretary of Education of the State of Rio de Janeiro (2007–2008). He was a President of the Brazilian Operations Research Society (SOBRAPO, 1989-1990), a President of the Latin-American Association of Operations Research Societies (ALIO, 1992-1994), and a Vice-President of IFORS (1998-2000). His research is funded by the Brazilian Council of Scientific and Technological Development (CNPq) and by the Rio de Janeiro State Foundation for Research Support (FAPERJ). He is the editor of six books and the author of more than 140 papers in international journals and 20 book chapters. He holds two US patents and has supervised 25 doctorate dissertations and 36 master theses. Dr. Ribeiro is the General Editor of the journal International Transactions in Operational Research. He is a member of the Brazilian Academy of Sciences and the coauthor of the book “Optimization by GRASP: Greedy Randomized Adaptive Search Procedures”, published by Springer in 2016.
GBCC- Grand Ballroom B, Level 3
USAFA Awarded 2017 UPS George D. Smith Prize
The U.S. Air Force Academy’s unique transdisciplinary undergraduate program exposes more than 50 percent of all students to at least one O.R. course, utilizes a year-long applied senior course for majors, and leverages its military faculty that rotate between analyst and teaching jobs to create a rich environment for learning and practice while actively advancing the Academy’s core mission “to educate, train, and inspire men and women to become officers of character motivated to lead the United States Air Force in service to our nation.”
Hilton- Ballroom of Americas, Level 2
How Analytics Powers the Uber Marketplace
Director of Data Science, Uber Technologies
Uber – the leading ride-sharing company in the world – faces the challenge of efficiently managing both sides of a two-sided market in which it has no direct control over either demand or supply. The challenges include determining real-time prices for both riders and drivers, matching riders and drivers and providing signals to drivers that incentivize efficient movement. In this talk we describe how Uber uses a wide variety of approaches drawn from operations research, management science, statistics, economics and machine learning to address these challenges in a highly dynamic temporospatial marketplace.
Dr. Robert Phillips is Director of Marketplace Optimization Data Science at Uber where he leads a group of more than 100 data scientists who develop and implement the analytics that empower Uber’s core businesses. Prior to joining Uber, Dr. Phillips was Professor of Professional Practice at Columbia Business School and Director of the Columbia Center for Pricing and Revenue Management.
Dr. Phillips is also the founder and a member of the Board of Directors of Nomis Solutions; a software and consulting company that helps financial service companies better manage pricing and profitability. Dr. Phillips has also served as CEO of Decision Focus Incorporated, a consulting company specializing in the application of business analytics in business and government and as CEO of Talus Solutions, a revenue management software and services company. During his 25-year career in industry, he was a pioneer in the application of pricing and revenue optimization in many different industries including passenger airlines, cruise lines, rental cars, automotive, hotels, air cargo, trucking, container shipping, and financial services.
Dr. Phillips is author of the book Pricing and Revenue Optimization and co-editor of The Oxford Handbook of Pricing Management as well as author of the forthcoming Pricing for Consumer Lending. Dr. Phillips holds a Ph.D. in Engineering-Economic Systems from Stanford University and Bachelor’s Degrees in Economics and Mathematics from Washington State University.
GBCC- Grand Ballroom B, Level 3
Looking to the Future by Celebrating the Past: Operations Research and Revenue Management in the Travel Industry
University of Houston
Today, the travel industry relies on sophisticated optimization algorithms working on vast data sets to operate profitably. But that wasn’t always the case. Commercial airlines took flight long before the advent of the computer. Inspired by the need to keep record of who was flying where, airlines were some of the earliest adopters of technologies to streamline the process, including a forerunner of the Internet. And once armed with historical booking records, visionary leaders realized how this information could be used as a competitive weapon for planning, scheduling, and pricing. From gumball dispensers to modern day revenue management systems, we trace the history of travel industry operations research in general, and revenue management practices in particular.
Dr. E. Andrew (Andy) Boyd is a scheduled contributor to the Engines of Our Ingenuity, a nationally syndicated program produced by Houston’s National Public Radio affiliate, KUHF, where he has authored and voiced nearly 400 episodes. He held tenure at Texas A&M University prior to taking the position of Chief Scientist and Senior Vice President at PROS, an analytics firm specializing in revenue management and dynamic pricing. He now serves as an adjunct faculty member in the industrial engineering department at the University of Houston.
Dr. Boyd received his AB with Honors in 1981 at Oberlin College with majors in mathematics and economics, and his PhD in operations research in 1987 from MIT. He has numerous scholarly publications to his credit, has written several technical articles and book chapters, and was part of a 2003 Edelman finalist team for work with Texas Children’s Hospital. He was elected an INFORMS Fellow in 2007, and shared the INFORMS Impact Award for pioneering work in revenue management in 2016. He is author of the book The Future of Pricing: How Airline Ticket Pricing Has Inspired a Revolution (Palgrave-MacMillan, 2007).
GBCC- General Assembly C, Level 3
Super-Human Strategic Reasoning: Libratus Beats Top Pros in Heads-Up No-Limit Texas Hold’em
Poker has been a challenge problem in game theory, OR, and AI for decades. No program had been able to beat top players in large poker games. In January 2017, our AI, Libratus, beat a team of four top specialist pros in the main benchmark, heads-up no-limit Texas hold’em, which has 10^161 decision points. Libratus is powered by new algorithms in each of its three main modules:1) computing approximate Nash equilibrium strategies before the event, 2) endgame solving during play, and 3) fixing its own strategy to play even closer to equilibrium based on what holes the opponents have been able to identify and exploit. The algorithms are domain-independent and have applicability to a variety of imperfect-information games such as negotiation, strategic pricing, cybersecurity, auctions, finance, and steering biological adaptation and evolution (e.g., for medical treatment planning). Joint work with my PhD student Noam Brown.
Tuomas Sandholm is Professor at Carnegie Mellon University in the Computer Science Department, with affiliate professor appointments in the Machine Learning Department, Ph.D. Program in Algorithms, Combinatorics, and Optimization (ACO), and CMU/UPitt Joint Ph.D. Program in Computational Biology. He is the Founder and Director of the Electronic Marketplaces Laboratory. He has published over 450 papers. He has built optimization-powered electronic marketplaces since 1989, and has fielded several of his systems. In parallel with his academic career, he was Founder, Chairman, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world’s largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings.
He is Founder and CEO of Optimized Markets, Inc., which is bringing a new paradigm to advertising campaign sales and scheduling in TV (linear and digital), Internet display, mobile, game, radio, and cross-media advertising.
His algorithms run the UNOS kidney exchange, which includes 69% of the transplant centers in the U.S.
He has developed the leading algorithms for several general game classes. The team that he leads is the current two-time world champion in computer Heads-Up No-Limit Texas Hold’em poker, and Libratus became the first and only AI to beat top humans at that game. He is Founder and CEO of Strategic Machine, Inc., which provides solutions for strategic reasoning under imperfect information in a broad set of applications ranging from poker to other recreational games to business strategy, negotiation, strategic pricing, product portfolio optimization, finance, cybersecurity, physical security, military, auctions, political campaigns, and steering evolution and biological adaptation (such as for medical treatment planning and synthetic biology).
He served as the redesign consultant of Baidu’s sponsored search auctions and display advertising markets. He has served as consultant, advisor, or board member for Yahoo!, Google, Chicago Board Options Exchange, swap.com, Granata Decision Systems, and others. He holds a Ph.D. and M.S. in computer science and a Dipl. Eng. (M.S. with B.S. included) with distinction in Industrial Engineering and Management Science. Among his many honors are the NSF Career Award, inaugural ACM Autonomous Agents Research Award, Sloan Fellowship, Carnegie Science Center Award for Excellence, Edelman Laureateship, and Computers and Thought Award. He is Fellow of the ACM, AAAI, and INFORMS. He holds an honorary doctorate from the University of Zurich.
GBCC- General Assembly B, Level 3
Optimization and Analytics Applications in the Oil and Gas Industry
The oil and gas industry is going through transformational times as reflected in the shale revolution, the gradual increase of the use of non-renewables, the recent drop in the oil prices, and the increasing efficiency in energy uses. With such changes, the industry is faced with several challenges in achieving its goals of efficient and environmentally responsible operations, production optimization, and capital cost reduction.
The oil and gas industry is often divided into three sectors: upstream, midstream and downstream. The upstream is mainly focused on activities related to the exploration and production of hydrocarbons. Such activities include searching for subsurface accumulation of hydrocarbons, drilling wells, and the production of those resources. On the other hand, the downstream sector is mainly concerned with the conversion of produced raw hydrocarbons into useful and useable products. Downstream activities include refining of petroleum crude oil, the processing of natural gas, and the distribution of refined products, such as gasoline, jet fuel, diesel oil, heating oil, fuel oils, lubricants, and many other petrochemicals products. The midstream sector comprises the transportation, storage, and marketing of crude oil, refined petroleum products, natural gas and petrochemicals. Transportation and logistics for the various products are carried out through a variety of modes including pipelines, rail, barges, oil tankers and trucks.
Optimization and analytics technologies are currently playing a vital role in enabling the oil and gas industry to achieve the above objectives. In this talk, I will review selective applications of optimization technology in the Oil and Gas Sector with focus on problem modeling and algorithmic development in all three sectors.
Cassandra is the Computational Sciences Function Manager for ExxonMobil Upstream Research Company. She joined ExxonMobil in 2000. She began her career in the Optimization Technology team where she co-developed optimization software for oil and gas investment portfolio planning and management. From 2010 to 2012 she led the Visualization & Workflow Integration team responsible for the in-house visualization engine and geoscience/engineering integration software platform. From 2011 to 2013, she worked as a skill area advisor for Computational and Applied Mathematics in the Computational Sciences Job Family. In 2013, she was named Computational Methods Architect, and from 2013 to 2016, she was the Computational Methods Supervisor.
Cassandra holds a bachelor’s degree in mathematics from Southwestern University and a master’s and PhD from Rice University.
GBCC- Grand Ballroom A, Level 3
2017 Daniel Wagner Prize Winner Announcement and Reprise
The Daniel H. Wagner Prize is awarded for a paper and presentation that describe a real-world, successful application of operations research or advanced analytics. The prize criteria emphasize innovative, elegant mathematical modeling and clear exposition. The competition sessions will be held Monday, October 23, in the George R. Brown Convention Center, Room 352E beginning at 11am.
GBCC- General Assembly A, Level 3
Reprise of 2017 Edelman Award-Winning Presentation
Revenue Management Delivers Significant Revenue Lift for Holiday Retirement
Revenue Management Delivers Significant Revenue Lift for Holiday Retirement
Revenue Management (RM) is a business discipline leveraging management science and information technology to drive bottom-line profitability. Holiday Retirement (Holiday) is the largest private owner and operator of independent living communities for seniors in the United States with over $1 billion annual revenue. Holiday partnered with Prorize LLC to change its pricing process completely using advanced revenue management (RM) algorithms starting 2011. Since it fully deployed the state-of-the-art system in 2014, Holiday has increased its revenue from new rentals by $88 million, or approximately 9 percent.
Ahmet Kuyumcu is the co-founder and CEO of Prorize LLC, a premier provider of revenue management solutions for the senior living, self-storage and other rental-pricing industries. Ahmet has directed and built profit-generating pricing systems across a broad range of sectors including senior living, self-storage, apartment, hotel, gaming resort, network television, telecommunication, distribution, manufacturing and retail.
Prior to Prorize, he served as Chief Scientist at Zilliant, and before that, as a Senior Scientist at Talus (now JDA). Ahmet also taught graduate-level classes in Pricing and Revenue Management at the University of Texas at Austin and the Indian School of Business. Ahmet has served on the boards for the Pricing and Revenue Management section of INFORMS, and the Journal of Revenue and Pricing Management. Ahmet earned his M.S. and Ph.D. degrees in Industrial Engineering from Texas A&M University.
GBCC- Grand Ballroom B, Level 3
A Probabilistic Theory of Deep Learning
A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with near- or super-human capabilities. But a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive. We answer this question by developing a new probabilistic framework for deep learning based on the Deep Rendering Model: a generative probabilistic model that explicitly captures latent nuisance variation. By relaxing the generative model to a discriminative one, we can recover two of the current leading deep learning systems, deep convolutional neural networks and random decision forests, providing insights into their successes and shortcomings, a principled route to their improvement, and new avenues for exploration.
Richard G. Baraniuk is the Victor E. Cameron Professor of Electrical and Computer Engineering at Rice University. His research interests lie in new theory, algorithms, and hardware for sensing, signal processing, and machine learning. He is a Fellow of the American Academy of Arts and Sciences, National Academy of Inventors, American Association for the Advancement of Science, and IEEE. He has received the DOD Vannevar Bush Faculty Fellow Award (National Security Science and Engineering Faculty Fellow), the IEEE Signal Processing Society Technical Achievement Award, and the IEEE James H. Mulligan, Jr. Education Medal.
GBCC- Grand Ballroom A, Level 3
Smarter Tools for (Citi)Bike Sharing: Cornell Rides Tandem with Motivate
David Shmoys & Shane Henderson
Cornell’s School of Operations Research and Information Engineering (ORIE) has been working with the bike-sharing company Motivate with an emphasis on its New York system Citi Bike since it began operations in 2013. Cornell ORIE provides data analysis and advice about strategy and operations to Motivate, which operates most of the leading bike-sharing programs in the United States. We will describe a suite of models and algorithms that provide data-driven decision-making tools not just for operations but also for strategic system planning.
Joint work that is part of the doctoral dissertations of Daniel Freund, Nanjing Jian, and Eoin O’Mahony, and with contributions from many more PhD, masters, and undergraduate students
Shane G. Henderson is professor and director (since July 2017) of the School of Operations Research and Information Engineering at Cornell University. He has previously held positions in the Department of Industrial and Operations Engineering at the University of Michigan and the Department of Engineering Science at the University of Auckland.
He is the editor-in-chief of one of INFORMS newest journals, Stochastic Systems. He has served as chair of the INFORMS Applied Probability Society, and as simulation area editor for Operations Research. He serves on several INFORMS award committees. His research interests include discrete-event simulation, simulation optimization, and emergency services planning.
David Shmoys is the Laibe/Acheson Professor at Cornell University in the School of Operations Research and Information Engineering, and also the Department of Computer Science at Cornell University.
Shmoys’s research has focused on the design and analysis of efficient algorithms for discrete optimization problems, with applications including scheduling, inventory theory, computational biology, and most recently, on stochastic optimization models and algorithms in computational sustainability. His graduate-level text, The Design of Approximation Algorithms, co-authored with David Williamson, was awarded the 2013 INFORMS Lanchester Prize. He is an INFORMS Fellow, a Fellow of the ACM, a SIAM Fellow, and was an NSF Presidential Young Investigator; he has served on numerous editorial boards, and is currently on the boards of INFORMS Journal on Optimization and Mathematics of Operations Research.