Plenaries and Keynotes

Sunday

Plenary

10 – 10:50am
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
Rice University

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.

Plenary – OMEGA RHO

3:10- 4pm
GBCC – General Assembly B, Level 3

Sports Scheduling Meets Business Analytics

Mike Trick
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. 

Monday

Plenary

10 – 10:50am
Hilton- Ballroom of Americas, Level 2

Optimization: Past, Present, Future

Bob Bixby
Gurobi

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.

Keynote

3:10 – 4pm
GBCC- General Assembly A, Level 3

INFORMing Process Improvement and Patient Safety in Healthcare

Victoria Jordan
Emory

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. 

Keynote

3:10 – 4pm
GBCC- General Assembly B, Level 3

Systems Approach to Managing Risk in Human Spaceflight Missions

Nancy Currie-Gregg
Texas A&M (formerly NASA)

Human spaceflight is an inherently risky endeavor. From the first human space exploration missions to recent problems on the International Space Station, NASA has faced many challenges and has relied on creative and innovative engineering solutions to complete the mission and safely return the crew. However, over the past 50 years, NASA has also experienced three fatal accidents resulting in the loss of seventeen crewmembers. Addressing the complex interdependencies and systemic causes associated with these tragedies, including those associated with organizational culture and management, provides insight to the advantages of a systems approach to managing risk in high reliability organizations.

Dr. Nancy Currie-Gregg spent the vast portion of her military and government career supporting NASA’s human spaceflight programs and projects. Selected as an astronaut in 1990, she accrued 1000 hours in space as a mission specialist on four space shuttle missions–STS-57 in 1993; STS-70 in 1995; STS-88, the first International Space Station assembly mission, in 1998; and STS-109, the fourth Hubble Space Telescope servicing mission, in 2002. A Master Army Aviator and member of the Army Aviation Hall of Fame, she logged over 4,000 flying hours in a variety of rotary-wing and fixed-wing aircraft.

Keynote

3:10 – 4pm
GBCC- Grand Ballroom C, Level 3

IFORS Distinguished Lecture

Biased Random-Key Genetic Algorithms: Components, Evolutionary Dynamics and Applications

Celso Ribeiro
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.

Keynote

3:10 – 4pm
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.”

Brigadier General Andrew “Andy” Armacost, Professor James “Jim” Lowe, Captain Drew Ives, and 2Lt Brady Gartman will present the 2017 UPS Smith Prize Reprise.

Tuesday

Plenary

9:40-10:30am
Hilton- Ballroom of Americas, Level 2

How Analytics Powers the Uber Marketplace

Robert Phillips
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.

Keynote

3:40 – 4:30pm
GBCC- Grand Ballroom B, Level 3

Looking to the Future by Celebrating the Past: Operations Research and Revenue Management in the Travel Industry

Andy Boyd
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.

Keynote

3:40 – 4:30pm
GBCC- General Assembly C, Level 3

Super-Human Strategic Reasoning: Libratus Beats Top Pros in Heads-Up No-Limit Texas Hold’em

Tuomas Sandholm
Carnegie Mellon
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.

Keynote

3:40 – 4:30pm
GBCC- General Assembly B, Level 3

Optimization and Analytics Applications in the Oil and Gas Industry

Cassandra McZeal
Exxon-Mobil

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.

Keynote

3:40 – 4:30pm
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.

Keynote

3:40 – 4:30pm
GBCC- General Assembly A, Level 3

Reprise of 2017 Edelman Award-Winning Presentation
Revenue Management Delivers Significant Revenue Lift for Holiday Retirement

Ahmet Kuyumcu
Prorize LLC

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.

Wednesday

Keynote

9:40 – 10:30am
GBCC- Grand Ballroom B, Level 3

A Probabilistic Theory of Deep Learning

Rich Baraniuk
Rice University

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. 

Keynote

9:40 – 10:30am
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. 

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.