UMN Industrial and Systems Engineering 2019

2019

INDUSTRIAL & SYSTEMS

ENGINEERING Distinguished in Teaching P. 32

ALSO INSIDE DRIVING CHANGE WITH DATA ANALYTICS

EXPLORING NEW FRONTIERS IN NETWORK SCIENCE PREDICTING THE FUTURE OF AUTONOMOUS VEHICLES

CONTENTS FEATURES

7

Data Driven ISyE faculty work on building a better tomorrow with data analytics

11

Optimizing Your Morning Coffee How ISyE researchers are modeling omni-channel systems built with everyone in mind

13

Hyperdrive As driverless cars move closer to reality, a U of M team examines what they will mean for the future

7

17

A Web of Connections ISyE faculty are exploring new frontiers in network science

DEPARTMENTS

3 Faculty 19 Undergraduate Program 24 Graduate Program 29 Alumni 31 Honors and Awards

24

29

19

ON THE COVER: ISyE Professor Lisa Miller, winner of the Horace T. Morse-University of Minnesota Alumni Association Award for Outstanding Contributions to Undergraduate Education.

Photo by Eric Miller

Department of Industrial and Systems Engineering 100 Union Street SE, Minneapolis, MN 55455

Department Head Saif Benjaafar

Director of Faculty and Academic Affairs Jean-Philippe Richard

Email: isye@umn.edu Phone: 612-624-1582

Director of Graduate Studies William L. Cooper

Department Administrator Hongna Bystrom

www.isye.umn.edu

Director of Undergraduate Studies Lisa Miller

Editor and Design Sam Schaust

1 Fall 2019 | Table of Contents

MESSAGE FROM THE DEPARTMENT HEAD

Industrial and Systems Engineering (ISyE) at the University of Minnesota is at the cutting edge of this transformation. Our faculty and students are involved in research that is developing advanced analytics to enable personalized medicine, omni-channel service, smart pricing, and driverless cars (read about these and more in our feature stories on the pages of this magazine).

This has been a great year for ISyE. The department has continued to grow. We welcomed four new faculty members and one of the largest incoming undergraduate classes. Our students continue to be popular with employers, finding jobs in tech, manufacturing, retail, and finance,

among others, and commanding some of the highest starting salaries in the College of Science and Engineering. We have launched our new M.S. in Analytics program and we are involved, in collaboration with the departments of Computer Science and Statistics, in the development of a soon-tobe-launched B.S. in Data Science.

The continued success of our department would not be possible without the support of the leadership of the college who has recognized the unique role of a department, within the College of Science and Engineering, that bridges the gap between engineering, business, mathematics, and computing to help deliver solutions for society's most pressing problems. We are also grateful to the support of our dedicated staff, engaged advisory board, our active industry partners, and our passionate alumni. To all of you, and to the many other friends and supporters of the department, a big thank you!

As usual, I will end with a request: please get in touch. Are you inspired by something you read here? Do you have an idea for a project?

Would you like to support student scholarships or faculty research? Please send me an email or give me a call. ISyE is growing and we want you to be a part of the story!

‘‘

Our faculty and students are involved in research that is developing advanced analytics to enable personalized medicine, omni-channel service, smart pricing, and driverless cars.

“

I

t's an exciting time to be an industrial engineer. Our field is at the heart of the transformation that is taking place in the economy, shaped by the revolution in big data and computing and advances in automation and artificial intelligence. Our field is also at the heart of many of the innovations in business models, from digital markets to smart mobility services to e-commerce. More than ever, the skills of industrial engineers (bringing to bear systems thinking and analytics to solve complex problems) are in demand, in domains ranging from healthcare to finance to supply chains, and many others.

—Saif Benjaafar,

ISyE Department Head

Message from the Department Head | ISyE Magazine

2

BY THE NUMBERS ISyE Undergraduate Enrollment

205

177

110 2015

2017

2019

$67,250 Median starting salary of ISyE graduates from UMN (based on 2017-2018 graduates)

Undergraduate Degrees Awarded in 2019 Graduate Student Enrollment

dergraduate n U er Outco me e ar

s

,o r

e at

in

du ar e

the

95%

Military

s

gra

3 Fall 2019 | By the Numbers

o f 2 01 8

59

C

(not including Fall term)

Em

ploy e

d, in Gra

e duat

Sc

ho

ol

61 graduate students 35 Master’s students 26 Ph.D. students

27 new enrollees in 2018 (2nd highest since 2015)

ADVISORY BOARD

Monder Ben Hamida

Eric Ayenegui Director, Operations Engineering Cintas Corporation

WW BPR Product Operations Apple

Member Since: 2019

Member Since: 2019

Sean Dervis

Megan Brosnan

Manager, Advanced Analytics Testing IDeaS Revenue Solutions Member Since: 2015

Member Since: 2015

Doug Houseman Senior Director, Supply Chain Engineering Target Member Since: 2019

Jeremiah Johnson

Brent Kellum

Senior Manager, Global Enterprise Excellence Consultant Boston Scientific

Scientific Director Mayo Clinic Member Since: 2019

Brian Naslund

Director of Strategic Pricing Daikin Applied

Director, Engineering Collins Aerospace Member Since: 2019

Member Since: 2014

Member Since: 2014

Kalyan Pasupathy

Member Since: 2019

Betsy Enstrom

Global Demand and Supply Chain Planning Capabilities Lead General Mills

Member Since: 2015

Chief Technology Officer Design Ready Controls

Member Since: 2019

Christine England

Director of Operations Pelican Biothermal

Mitchel DeJong

Director of Engineering Process Development Heraeus Medical Components

John Zaic Northern Plains IE Training and Development Manager UPS

Jeffrey T. Zudock Global Improve Manager ExxonMobil Member Since: 2018

Member Since: 2019

Advisory Board | ISyE Magazine

4

FACULTY

Saif Benjaafar Department Head, Distinguished McKnight University Professor PhD, Purdue, 1992 Operations management, supply chains, service systems, sharing economy, sustainability

William Cooper

Sherwin Doroudi

Director of Graduate Studies, Professor

Assistant Professor PhD, Carnegie Mellon, 2016

PhD, Georgia Tech, 1999 Stochastic modeling, pricing, revenue management, applied probability

Stochastic modeling, queuing systems, computer security

Qie He

Darin England Teaching Assistant Professor PhD, University of Minnesota, 2006

Krishnamurthy Iyer

Assistant Professor

Associate Professor

PhD, Georgia Tech, 2013

PhD, Stanford, 2012

Optimization, computation, transportation, healthcare

Game theory, applied probability, economics and computation, stochastic modeling

Optimization, simulation, machine learning

Kevin Leder

Zhaosong Lu

Ankur Mani

Associate Professor

Professor

Assistant Professor

PhD, Brown, 2008

PhD, Georgia Tech, 2005

Stochastic modeling, cancer evolution, probability theory

Theory and algorithms for continuous optimization, applications in data science, information engineering, machine learning, statistics

PhD, Massachusetts Institute of Technology, 2013

5 Fall 2019 | Faculty Directory

Peer and network interactions, pricing, matching and mechanism design

Lisa Miller

Dan Mitchell

Director of Undergraduate Studies, Teaching Professor PhD, Georgia Tech, 2002

Assistant Professor

Assistant Professor

PhD, University of Texas, 2014

PhD, Stanford, 2014

Financial engineering, stochastic control, option pricing

Optimization, operations research, analytics

Zizhuo Wang

Jean-Philippe Richard

Diana Negoescu

Healthcare operations and management, stochastic modeling, simulation

Shuzhong Zhang

Professor

Associate Professor

Professor

PhD, Georgia Tech, 2002

PhD, Stanford, 2012

Mathematical optimization, healthcare, transportation, infrastructure

Pricing, revenue management, optimization, Internet economics

PhD, Erasmus University Rotterdam, 1991

Yiling Zhang Assistant Professor PhD, University of Michigan, 2019 Optimization under uncertainty, energy systems, transportation, healthcare operations

Nonlinear optimization, game theory, signal processing, risk management

Tony Haitao Cui

Karen Donohue

Deputy Associate Dean for Global DBA

Board of Overseers Professor of Operations and Management Science

Carlson School of Management Affiliated Faculty

Carlson School of Management Affiliated Faculty

Faculty Directory | ISyE Magazine

6

ISYE FACULT Y WORK ON BUILDING A BETT ER TOMORROW WIT H DATA ANALYT ICS

By Joel Hoekstra

Data is accumulating around the world. By 2025, according to one recent study, each connected person on the planet will have at least one data interaction, from a Facebook like to a Google search, every 18 seconds. The opportunities for corporations, heathcare providers, educational institutions, governments, and other organizations to benefit from this information is enormous—if they can harness it.

Data analytics is increasingly central to the research and teaching of University of Minnesota ISyE faculty. Their interest is driven by

their own curiosity, but also fueled by questions arising in industry and business. Opportunities for ISyE students to study data analytics in their ISyE coursework continues to expand, and employers are eager to hire graduates with skills in this area.

"It's been a hot area and it's not cooling off," says ISyE professor Bill Cooper.

"I only see the demand going up."

This article is the first in a series focused on the wide-ranging work of ISyE department faculty in the area of data analytics.

REFINING REVENUE MANAGEMENT In some ways, data analytics is merely a contemporary form of traditional operations research, where quantitative methods are used to guide decision-making within an organization.

"What's changed over the past 10 to 20 years, is the scale at which these methods can be deployed," says ISyE professor Bill Cooper. "There's so much more data available, and there's significantly more computing power to take advantage of this data."

Cooper has a longstanding interest in models that help organizations with revenue management, and several of his research projects have centered around pricing in the airline industry. "Airlines were among the leaders in analyzing data, because they collected a lot of it and they had the ability to dynamically change their prices," Cooper explains.

"Now, all sorts of industries have information about previous buying histories, customer demand, competitor pricing, and more."

“Now, all sorts of industries have information about previous buying histories, customer demand, competitor pricing, and more. �

Bill Cooper , ISyE Professor

That data, some of it provided by customers themselves, helps airlines, retailers, and others predict demand and set prices accordingly. Such projections may lead an airline, for example, to change ticket prices on a certain flight, maximizing the profit potential.

Computing power has also grown by leaps and bounds in recent years, allowing data scientists to take advantage of more data than ever before. "The scale of problems you can address where the answer is not a formula has gotten bigger," Cooper says.

"Of course, the more things you put into a model, the more you have to estimate–so 'more complicated' is not always better. But in the past, even if you had a nice model for multiple products, you had no hope of doing computation. I suppose that there will always be data sets that are too large for certain types of analyses. But as computing power increases, it is possible to do more and more."

Feature Stories | ISyE Magazine

8

Data Analytics: The Broad View As data collection becomes ubiquitous—from online forms to electronic health records to the Internet of Things—the desire to glean knowledge from the aggregated data points increases. In some cases, it can help us understand what has already happened. In other cases, it can help us predict the future. And in still other cases, it can guide decision-making.

The field of data analytics can be roughly divided into three categories:

Descriptive Analytics As the name implies, descriptive analytics processes raw data from the past so we can understand it. Most statistics—including sums, averages, and percentage changes—fall into this category. We can learn about and understand past behaviors from descriptive analytics.

Predictive Analytics The future is the focus of predictive analytics. Past data is used to estimate future outcomes. No statistics can predict the future perfectly, of course, but data can help us estimate likelihoods. Businesses use predictive analytics to forecast sales and predict what customers will buy in the future.

Prescriptive Analytics Data analytics aimed at shaping decision-making is known as prescriptive analytics. In other words, data is used not only to make predictions but also to make recommendations. Prescriptive analytics can be used to optimize everything from supply-chain scheduling to the customer experience.

9 Fall 2019 | Feature Stories

IMPROVING MEDICAL T REAT MENT WIT H DATA Data analytics is also changing the face of healthcare and medicine. For example, ISyE Professor Kevin Leder recently began a collaboration with researchers at the University of Oslo to determine the best course of treatment for patients with multiple myeloma. Often, after a period of use, a drug will no longer work effectively for a patient, so providers must decide which drug to use next.

"There are a lot of drug choices," Leder says. "After the patient has gone through ten drugs, it's kind of a guess what drug should be used for the best outcome.

Our goal is to create algorithms that use multiple myeloma patient history and in vitro experiments to determine a recommended course of therapy for the patient."

Data analytics has improved greatly with the advent of machine learning, Leder says. Data sets have gotten better and computing power has increased.

"We're learning the answers to questions that we couldn't answer all that well before," he says. "Eventually, we hope to get to the point in medicine where we can say, yes this is the right treatment, based on data."

But the effectiveness of data analytics, of course, is based on its design. One challenge in determining the best course of treatment is the need to predict outliers. "Tumors are generally very complex and heterogeneous entities, so we have a problem where sequencing the tumor doesn't tell us the whole picture," Leder says. "Imagine a tumor has 100 billion cells and you sequence the tumor to know what drug to take. Your sequencing says take drug A, but there is a subpopulation in the tumor of size 100 million cells (0.1% of cells) that is resistant to drug A. It is hard to imagine a way we can figure out how to identify the presence of that 'tiny' subpopulation resistant to drug A. I think we'll figure it out eventually, but right now it seems hard."

Teaching Data Analytics Students interested in data analytics have a number of courses they can choose from within the ISyE Department. Among the courses recently launched are Analytics and Data Driven Decision Making, Optimization for Machine Learning, and a special topics course on Analytics for Personalized Medicine. Some of these courses may soon be integrated into a B.S. in Data Science degree program, being planned in collaboration with

the Computer Science and Statistics departments.

deeper into the technical details regarding decision-making.”

ISyE’s new MS in Analytics program graduated its first batch of students in the 2018-2019 school year. Launched in 2018, the 30-credit, three-semester degree program differs from similar master’s level programs at the university because it focuses on prescriptive analytics, says Bill Cooper, Director of Graduate Studies for ISyE: “We delve

Students enrolled in the program participate in semester-long capstone projects sponsored by industry and supervised by faculty and industry mentors.

Cooper expects the program to grow quickly in the next few years, as demand for data-analytics expertise increases. “I think this has the potential to become an extremely popular program,” he says.

HONING HEALT HCARE DIAGNOSES WIT H DATA Increasingly, the data available to researchers is so overwhelming that it's hard to know what to look for at first. Fortunately, machine learning provides an opportunity to search for the proverbial "needle in the haystack."

ISyE Professor Shuzhong Zhang is among those who are hoping to discern patterns in medical data that will allow medical providers or physicians to predict conditions like lung cancer and Alzheimer's in their early stages—when intervention can significantly alter the outcome for the patient. "It's easy to gather the data, but difficult to figure out how to meaningfully compare indicators from

“Data is the way to help us out of this problem. ” Shuzhong Zhang , ISyE Professor

different sources," says Zhang, whose specialty is the design and analysis of optimization algorithms.

Zhang is collaborating with researchers at Arkansas State University to discern patterns in DNA sequences and CT images that, taken together, can alert doctors to the likelihood of Alzheimer's.

"CT scans are very large, which can be a challenge," Zhang says. "The number of variables can be in the millions, and there may be a lot of noise in the data."

DNA sequences are more straightforward, but figuring out how to use the data jointly is the ultimate—and as yet elusive—point of the project.

"If we can unlock the connection, we can reinforce confidence in our predictions of early stage Alzheimer's," Zhang says. "Data is the way to help us out of this problem."

As our ability to use markers to predict disease improves data analytics my lead us to individualized medicine, Zhang says. "Perhaps someday we'll be designing specific cancer treatments for individuals," he says.

Joel Hoekstra is a Minneapolis-based writer.

Feature Stories | ISyE Magazine

10

ptimizing Your Morning Coffee HOW ISYE PROFESSOR SHERWIN DOROUDI AND PH.D. STUDENT KANG KANG ARE MODELING OMNI-CHANNEL SYSTEMS BUILT WITH EVERYONE IN MIND

I

magine this scenario: You open the door to a Starbucks and jump into a line that is sixpeople deep. Entering through the door behind you is someone on their phone. You see them tapping on the screen and step aside, but not into the line. Before you have the chance to say your order or swipe your credit card, the person on their phone is picking up their coffee and sipping it as they exit. Is this fair?

If you ask queueing theory expert and ISyE Professor Sherwin Doroudi, there is no one-size-fits-all method for businesses to follow when prioritizing in-person and online orders, also known as an omni-channel system. For Starbucks alone, this could raise questions such as, "Which orders do you prioritize first—walk-in or mobile?" and "When should the staff be taking new orders as opposed to fulfilling outstanding ones?"

By Sam Schaust ISyE Professor Sherwin Doroudi (right) and Kang Kang discussing their research at a local coffeeshop. (Photo by Sam Schaust)

"It's really contextually driven," Doroudi says. In their recent paper, "Designing Efficient and Equitable Omni-channel Service Systems," Doroudi, ISyE Ph.D. student Kang Kang, and their collaborator at Stony Brook University, Professor Mohammad Delasay, are developing and analyzing omni- channel service models. Their models consider a

1. The arrival rate, or traffic, for a service

Complexities of the Coffee Shop Model Breaking down the questions Doroudi and Kang considered in their research

▶ How much demand for coffee is coming

through the door and from mobile orders?

2. Time to complete each service

▶ How long does it take to receive an order via walk-in or through a mobile app?

▶ How long does it take to deliver each order? ▶ How long does this differ between quick

service locations and a business with longer queues, on average?

3. Mobile versus walk-in preferences

▶ Do customers of a certain store more strongly prefer ordering via mobile than waiting in line?

▶ How do these preferences differ from store to store?

number of factors, including customer patience levels, demand, workers on hand, and other complexities (see sidebar).

Among their initial findings, Doroudi and Kang have discovered that sometimes a mobile ordering option can be bad for business, even when making the right prioritization decisions. "That was what we found most surprising," Doroudi says. "Allowing users to submit mobile orders on their own should free up staff time, which it does. Yet, due to subtleties uncovered by our research, it can actually lead to more customers opting out of a service for fear of lengthy wait times."

To construct their models, Doroudi and Kang chose coffee shops as their archetypical example. Their reason was simple: most modern coffee shop orders filter in through two streams,— online or in-person. Developing stochastic and optimization models in this environment would be much simpler than a healthcare clinic or government agency, for example, which typically feature multi-step processes.

But for Doroudi and his

collaborators, there's far more than coffee at stake.

"We don't just want to make things as best as possible for the business, the service provider, the mobile crowd, or the walk-in crowd," Doroudi says. "We want to look at all stakeholders and see what are the trade-offs."

In their research, Doroudi, Kang, and Delasay determined there is the potential for unintended discriminatory treatment against low income, disabled, and other individuals who are less likely to have access to smartphones or cannot use a mobile app. Ensuring equitable outcomes will require paying careful attention to how each stakeholder is treated under a given operational recommendation.

While saving or losing a couple of minutes at the coffee shop every now and then may not mean much for most people in the grand scheme of things, the impact on waiting times is much more critical in healthcare and government systems, which feature similar (albeit more complicated) system dynamics.

In healthcare, for example, newly

4. Level of customer patience

▶ Based on the line ahead of them, how long are customers willing to wait for a service?

▶ At what point is a line too long where

customers begin to leave?

5. Number of workers in various roles

▶ In a coffee shop, how many staff of each type (cashiers, baristas, and those who alternate between both roles) are working at once?

▶ How can more or fewer employees on hand affect business operations?

6. Type of service provider

▶ How do operations of an omni-channel system differ between coffee shops and other types of companies and organizations?

emerging mobile health and self-diagnostic technologies are beginning to allow some patients to bypass lines and procedures at the clinic, thus freeing up provider resources. But these technologies will (at least initially) be available primarily to those from the wealthiest backgrounds.

How can the potential benefits of these technologies be balanced with the additional delays they impose on those who are less fortunate? How (and to what extent) can those additional delays be mitigated?

Expanding the scope of their omni-channel models to answer these kinds of questions in the context of hospitals, government agencies, and nonprofits, is precisely where Doroudi and Kang plan to turn next.

"We've been developing new tools and new ways of looking at things," says Doroudi. "Once we have the full picture, we can show people a graph or a formula to tell someone if it's better to offer omni-channel at all and whether walk-ins should be prioritized.

These new ways of looking at these systems we're developing could actually be one of the important findings of our research."

Feature Stories | ISyE Magazine

12

hyper drive

13 Fall 2019 | Feature Stories

AS DRIVERLESS CARS MOVE CLOSER TO REALITY, A U OF M TEAM EXAMINES WHAT THEY’LL MEAN FOR THE FUTURE

BY JOEL HOEKSTRA

Last April, a blue-and-green electric bus with tiny wheels

OFF THE MAP

and space for 12 passengers appeared on the Washington

Such questions have brought together a team of researchers

Avenue bridge in Minneapolis. Guided by a remote sensing

led by Zhi-Li Zhang, who holds the endowed Qwest Chair in

system, the driverless vehicle shuttled riders back and forth

the U’s College of Science and Engineering (CSE), to search

between the University of Minnesota’s East and West Bank

for answers. In October, the U received a three-year $1.75

campuses during the course of a day. With a maximum speed

million Smart and Connected Communities grant from the

of 25 miles per hour, the bus was barely faster than many of

National Science Foundation (NSF) to study the potential

the bicyclists crossing the span.

impact on communities. The U’s initiative is one of 13 nation-

But for a handful of U researchers, it was a reminder that the age of autonomous vehicles is racing toward us at top ILLUSTRATIONS BY WILLIAM RIESER; BUS PHOTO: MICHAEL MCCARTHY, CENTER FOR TRANSPORTATION STUDIES

speed—and in many ways, our society and infrastructure are woefully unprepared for its arrival.

Take, for example, our roads, says Tom Fisher, Dayton Hudson Chair in Urban Design and director of the U’s Minnesota Design Center. “We’ve already seen that roads are developing ruts because autonomous vehicles are so precise in their travel patterns,” he says. “That means our current streets won’t work for them.”

Rutted roads are far from the only challenge autonomous vehicles (AVs) could bring. Driverless cars and trucks raise new questions about how we live, travel, work, and connect.

Will we own vehicles or share them? How will we ensure that everyone is served by the new AV infrastructure? Who will regulate it? And who will pay for it?

wide to receive the award.

This self-driving electric bus was tested on the U of M campus as part of a Minnesota Department of Transportation study.

Some companies, like Uber and Waymo (a division of Google), have invested heavily in AV research and have a stake in its development. But Saif Benjaafar, McKnight Distinguished University Professor and ISyE Department Head, says research institutions like the University of Minnesota can provide a more unvarnished and comprehensive perspective on what such technologies mean for our collective future.

“We don’t have skin in the game,” he says, “so we can be a resource to policymakers and others as they think about the future and how they approach regulation.”

The U comes to the AV study with a long history of finding ways to improve transportation and make it safer. In the late

ing AVs. The faculty involved in the AV project, nearly all of

1

whom are supported by endowed chairs that give them the

Center for Transportation Studies and director of the

time and latitude to dive into such timely topics, come from

Humphrey School’s State and Local Policy Program. “In

the Center for Transportation Studies as well as the College of

reality, they may not have to buy one at all.”

1950s, U of M researcher James “Crash” Ryan invented the retractable seat belt and other innovations. The U’s Center for Transportation Studies, formed in the late 1980s, is wellknown for its studies of transportation-related issues, includ-

Design, Humphrey School of Public Affairs, and CSE, bringing

You probably won’t own a vehicle.

“If you mention self-driving cars to most people, they think, ‘Oh great, the next car I buy is going to cost $10,000 more because I’m going to have to pay for the

technology,’” says Frank Douma, a research fellow in the

Benjaafar points to the so-called “sharing economy” as

a range of perspectives. No one can predict the future, of

a sign of things to come: For many people in urban areas,

course, but here are some of the ways these researchers be-

using app-based ride services like Uber or Lyft is easier

lieve AVs will affect individuals and communities:

and more economical than owning a car, finding parking, and paying for gas and insurance. As the world transitions to AVs, Douma and others believe it’s more likely that we’ll ride in cars owned by someone else—probably a company like Waymo—and pay per trip or through a subscription service.

what are

autonomous vehicles?

2

The cloud will steer your car.

Proponents of AVs say the technology eliminates congestion

by replacing human decisions with a centralized control system that determines the most efficient way to get all vehicles to their destinations. Cars wouldn’t have to stop at intersections, and a “smart cloud-based commuter system” (SCCS) would slow or speed each vehicle to prevent crashes.

“These cloud systems have the potential to bring about far-reaching changes,” Zhang says. But if car companies

Autonomous vehicles fit into the following categories:

operated independent fleets of vehicles, who would be

LEVEL 1: Has features such as adaptive cruise control and lane-keeping assistance

responsible for ensuring a secure connection to the SCCS?

LEVEL 2: Can assist with acceleration or steering LEVEL 3: Monitors the road and traffic and lets the driver know when to take control LEVEL 4: Self-driving if conditions permit LEVEL 5: Completely self-driving Source: Governor’s Highway Safety Association

Who would manage the system, have access to AV data, and provide a backup system for safety—the federal government, municipalities, or a private contractor?

3

Flowers might grow in the streets.

Because the technology that controls AVs is so precise, the width of

our roads could be reduced, Fisher says.

What’s more, a SCCS could direct traffic so efficiently that just one (or two, at most) lanes could accommodate a large volume

15 Fall 2019 | Feature Stories

of vehicles. Fisher imagines that highways will look more like railroad beds, with two hard-surfaced parallel paths supporting the wheels of vehicles. That means more space could be

freeing up farmers Autonomous vehicles are already working farm fields.

Drones, driverless tractors, and small robots are being used to plant, spray, and monitor crops.

devoted to bike lanes, walking paths, or even gardens.

“Much of the street surface could be pervious,” Fisher says. “It could be green. It could be gravel. Runoff won’t have to go into a storm sewer system and pollution won’t

Researchers from the West Central Research and Outreach Center (WCROC) in Morris, Minnesota, are leading the development of two autonomous farm vehicles—one that will control weeds in pastures and another that will eliminate them from row crops. Both will run on solar power.

be swept into our rivers.”

4

Parking lots, meters, and garages will vanish.

Roughly a third of all land in urban areas is used for parking and storing

The WCROC team, which is part of the U’s College of Food, Agricultural, and Natural Resource Sciences, is working on this project with staff from the U’s Department of Computer Science and Engineering and Department of Bioproducts and Biosystems Engineering, and The Toro Company.

cars. Surface lots, street spaces, parking garages, and even your home garage could be repurposed in a world where a fleet of AVs are in constant circulation. “These cars can go away at night, park themselves outside the city, and then be called back when needed,” Benjaafar says.

Fisher sees an opportunity to replace parking structures

The two vehicles will be field-tested in WCROC’s pastures during the summer of 2020.

with affordable housing—which is increasingly in short supply as the urban population grows. As for home garages, he says homeowners will still need a place for bikes, lawnmowers, and snow shovels.

5

the Humphrey School and Center for Transportation Studies.

Vehicles will be places for working, sleeping, crafting.

“Transportation has direct connection to health,” she says.

If you don’t have to pay attention to

central to our basic living needs. It allows us to participate in

traffic on your ride to work, you’ll

recreational and social activities, both of which are important

be free to do other things. Fisher believes manufacturers will capitalize on that

“It provides access to health care and medical clinics and is

for individual health.”

Fan and others worry, however, that without proper incen-

idea, and that we’ll see vehicles designed

tives or regulation, some communities will be left behind. Pol-

for particular activities such as deliver-

icymakers will have to ensure that ride providers don’t serve

ing groceries. “Mercedes Benz is looking

only affluent communities. They’ll also have to consider the

at cars that are also meeting spaces, so instead of driving to

potential impact on rural areas, which may not have enough

your meeting you actually have the meeting in the vehicle,” he

people to attract ride providers or the funds to build digital and

says. Happy hour could begin on the way home, in a specially

physical infrastructure. Fan believes communities need to

equipped bar car.

create a collective vision for how AVs will work in their locale.

6

“Not all questions can be answered with computer simulation,”

Your car will run your errands.

she says.

Will AVs make us more produc-

CARRIAGES, CARS, AND BEYOND

tive? Imagine a world where

The research and analysis generated by the three-year

AVs deliver your groceries, drop the

NSF-supported study will guide future leaders and lawmakers

kids at soccer practice, and pick up

as they make decisions about AVs that affect communities and,

your dry cleaning. “When I’m not

in fact, entire countries. More specifically, the U of M team

using it, the car can do other things for me,” Benjaafar says. “It’s

would like to develop a handful of simulations that would

going to unleash a lot of hours in the day that we’d otherwise

illustrate how choices regarding AVs might play out.

spend driving or sitting in traffic. That could reduce frustra-

The College of Design’s Fisher likens the dawn of the AV

tion and improve overall well-being.”

age to the change that happened a century ago, when motor

7

vehicles replaced horses. “We took one animal out of our trans-

Everyone will be able to hitch a ride.

portation system and replaced it with something that was

AVs offer huge potential benefits

cheaper, safer, and cleaner,” he says. “Now we’re taking another

to the elderly, disabled people, and

animal out of the system—humans—and getting a solution

others who cannot drive—and easy

that’s cheaper, safer, and cleaner.”

access to transportation can have a profound impact on their physical health

Joel Hoekstra is a Minneapolis writer.

and social well-being, says Yingling Fan, a McKnight Land Grant professor in

This article was originally published in the Winter 2019 issue of Legacy.

Feature Stories | ISyE Magazine

16

A WEB OF CONNECTIONS

“

In the last 10 years, there has been a lot of focus on understanding how network structures can be used for previously unconsidered purposes: controlling epidemics, designing better transportation systems, increasing physical activity, reducing waste. We are finding broader and beneficial applications for network design.

By Joel Hoekstra

�

Ankur Mani, ISyE Professor

ISyE faculty are exploring new frontiers in network science

A

half-century ago, the social psychologist Stanley Milgram conducted an experiment that vividly demonstrated the power of social networks. Milgram sent packages to 160 random individuals living in Wichita, Kansas and Omaha, Nebraska, asking them to forward the items to someone they knew on a first-name basis, with the ultimate intent of getting the mail to a stockbroker in Boston. On average, the packages passed through the hands of five intermediaries before they reached the intended recipient.

The conclusion of the “Small World” experiment—the idea that there are “six degrees of separation” between any two individuals on earth—has been roundly criticized in the ensuring decades, but there’s no disputing that Milgram was on to something: networks can function in astonishing ways.

Networks—both social and otherwise— surround us today. Large societal networks such as electrical power grids, communication networks, and transportation networks are all overlayed on social networks. The structure and properties of these large societal networks are governed by decisions made by individuals in the social networks. Other networks are as old as time: the synapses that wire our brains, the biological relationships that govern ecosystems around the planet. And at the intersection of physical, biological, and social networks are multi-layer networks that influence consumer choices, business decision-making, and workplace behaviors. These are particularly interesting to network science researchers like Mani.

Mani, who teaches courses on network science for ISyE, says companies and small businesses alike have long relied on social networks to help bring them customers and generate

sales. (Many businesses, for example, offer rewards to customers who refer friends.) The Internet, of course, significantly expanded business interest in networks: Today, companies use algorithms to analyze networks and use the findings to set prices and hone their marketing efforts.

But understanding how networks function can be tricky, Mani says.

Social behaviors are predictable, but also complex. “Lots of companies give discounts to influencers on Instagram in the hope of getting them to promote their products and drive sales,” Mani says.

“But does it really work?” In a 2019 paper titled “The Value of Price Discrimination in Large Random Networks,” Mani and his coauthors examined the practice and found that as the influencers’ networks grew bigger, the less beneficial the programs were, considering their cost and impact. What’s more, Mani says, “In some cases such programs lead to distrust among customers. If I find out my friend is getting a lower price than I am, I’m not going to be very happy. Considering the cost of implementation, the extra benefit you achieve is minimal.”

“

Networks can be huge and multi-layered. Our ability to study them in a systematic way is fairly new—but the more we learn, the more we can understand the underlying patterns of behavior.

”

Krishnamurthy Iyer, ISyE Associate Professor To understand how social networks function, researchers have to assess not only the nodes (i.e. the individuals) in the network, but also how they link

to each other. These links, sometimes called edges, indicate how the nodes relate, says ISyE professor Krishnamurthy Iyer. “If the network represents parent-child relationships, for example, then the links are asymmetric,” Iyer says. “To understand a network, you have to understand how the components relate to and influence each other.” If you receive a referral for a psychologist from a close friend, for example, are you more likely to use it than if it came from a neighbor? Whose advice would you take regarding a stock tip—a stranger’s or a stock-broker’s?

Social networks are sometimes blamed for the spread of bad phenomena, like fake news or Ebola. But Mani observes that social networks can also be used to create good outcomes, such as energy conservation and improved physical activity. Traditional approaches to change behavior would involve individual incentives (Pigouvian mechanisms).

“But this would not be as efficient as using peer pressure,” says Mani, who has studied the effects of inducing peer pressure to promote cooperation, such as in his paper titled “Inducing Peer Pressure to Promote Cooperation.” The more effective solution, he says, is to come up with a system where citizens are more likely to benefit if they can get other citizens to adopt good habits. “The peer pressure mechanisms are more efficient than individual incentives,” Mani explains.

Our understanding of how networks function has grown significantly in recent years, Iyer says, and will likely accelerate as our ability to collect data and harness computing power increases. “Networks can be huge and multi-layered,” Iyer says. “Our ability to study them in a systematic way is fairly new—but the more we learn, the more we can understand the underlying patterns of behavior.”

Joel Hoekstra is a Minneapolis-based writer.

Feature Stories | ISyE Magazine

18

UNDERGRADUATE STUDENT SPOTLIGHT

A Big Picture Co-op Experience Senior Emma Ehling gained a

Her task was to create instruc-

facing the company: the shipping

wealth of real-world experience

tion documents for the assembly

of wrong parts. Ehling built a new

during her 2017-2018 school year as

process.

document

a co-op at Legrand AV.

“I learned a lot about data

Her first project for the Minne-

analysis, communication, change

sota-based electrical equipment

management, and general project

manufacturer was geared toward

management,” she says of the

its Shakopee plant, where over

experience. After her first project’s

100

success,

workers

assemble

mounts

she

transitioned

to

and stands for projectors, TVs,

Legrand AV’s Minnetonka facility

computers, and other equipment.

to

correct

a

significant

issue

management

system

to prevent the shipping errors, which she called “an insightful experience” that she built upon in her next role as an intern at West Monroe Partners. When Ehling graduates in May 2020, she will begin her career at West Monroe Partners as a full-time worker.

Decorated with Awards

Photo: Rickey Sipila

ISyE student Rickey Sipila with the Eos II.

Racing Across the Outback This October, the UMN Solar Vehicle Project student group raced its car across the Australian Outback in the Bridgestone World Solar Challenge.

The weeklong race featured nearly 50 student-designed vehicles from more than a dozen countries. UMN Solar Vehicle’s car, named Eos II, was one of only four solar-powered vehicles from the U.S. that competed. As the group’s co-president last year and director of operations this year, ISyE student Rickey Sipila has made it his mission to see the UMN Solar Vehicle Project thrive after his graduation.

“A challenge we face is that we rarely have members that stay more than four years because of graduation,” he said.

“Knowing the importance of continuous improvement, I have created video tutorials and documentation to help the next generation,” he says. The document includes a financial tracking system, organized marketing and sponsor outreach tactics, streamlined event coordination, and Student Service Fee preparation.

Visit z.umn.edu/ UMNSVP-Australia-2019 to experience the UMN Solar Vehicle Project team’s 3,000 kilometer journey from Darwin to Adelaide in Australia.

To date, Sipila’s efforts have improved the group’s revenue by $15,000 and increased the number of events in which they participate by 25 percent.

UMN Robotics was the recipient of several awards over the last year, including the 2018 Tony Diggs Excellence Award for “Outstanding Undergraduate Student Group” and the 2019 Tin Man Award for “Most Improved Student Group.” The students also won “Best Student Paper” Award and the “Collaborative Operational Challenge” during the 2019 ION Autonomous Snowplow Competition. With the NCAA Final Four 2019 men’s basketball tournament taking place in Minneapolis, UMN Robotics built a basketball shooting robot and competed in the Land O’Lakes Bot Shot Championship. Their creation appeared at the Minnesota State Fair over the summer.

Mentorship for Undergrads In October, the Institute of Industrial and Systems Engineers (IISE) kicked off a new mentorship program for undergraduates. The year-long offering pairs its members with industry professionals to discuss career goals, internship and research opportunities, student group activities, technical electives, and to generally share advice to make the most of the college experience. “The overall goal of the mentorship program is to provide current and prospective ISyE underclassmen with first-hand resources [and] the opportunity to gain long-term leadership experience,” said Rachel Kukielka, secretary of the University of Minnesota’s IISE chapter.

Undergraduate Student Group Highlights | ISyE Magazine

20

Senior Design Projects

E

ach year, ISyE seniors work in groups of three to five to address important issues affecting local and national businesses and organizations.

In Spring 2019, nine organizations sponsored thirteen Senior Design projects. Project teams conducted site visits—including one to California—collected data, developed models, and performed analysis to provide the sponsors with fresh perspectives and insights on ways to address important challenges. Simultaneously, students practiced hands-on approaches to solving problems in a real-world setting.

TARGET

Challenge Alleviate the bottlenecking issues caused

by Pack to Store goods shipments, which are occurring daily at Upstream Distribution Centers.

Outcomes The team identified five potential solutions, steps to implementation, and a break-even analysis for each suggestion that would improve the throughput rate of the Pack to Store process.

What follows is each student group’s industry sponsor, their project title, and a handful of highlights from this year’s Senior Design Projects.

All Flex Inc. Improved Inventory Tracking + Kanban Card System Expansion Boston Scientific Corp.

Material Drop Optimization and Part Presentation Improvement + Optimizing Workflow of Boston Scientific’s MTAC Lab Daikin Applied Potential Cost Savings Tool + Pipeline Data Analysis and Modeling Heraeus Medical Components Lean Tools Process Improvement

21 Fall 2019 | Senior Design Projects

Protolabs Operation Capacity Planning Tool + Quality Measure System Analysis Scott County Household Hazardous Waste Facility Optimization Syngenta Seed Inventory Management Target Upstream Distribution Center Improvement West Monroe Partners Neighborhood Health Source Group Purchasing Initiative

HERAEUS MEDICAL COMPONENTS

Challenge Optimize the manufacturing process of Heraeus' new micro-molding cell,—an object used to create medical device battery connectors.

Outcomes The team proposed splitting the tasks of one operator role between two operators and justified this change by providing data and carrying out a financial analysis. They also suggested introducing a new tray that was better designed to shorten the non-value-added process of picking up and placing unfinished parts during the manufacturing process.

Comments from Project Sponsors SCOTT COUNTY

PROTOLABS

Challenge

Challenge

Improve hazardous waste sorting procedures and analyze the growth trends for a public-facing waste facility that does not have the capacity for physical expansion, despite population growth in the county.

Reduce variation, or reject rates, in the end-to-end manufacturing process of 3D computer-aided design models between weekday and weekend shift operators.

Outcomes

Outcomes

The team analyzed historical data and created a simulation to forecast changes in material throughput through 2030. Recommendations included implementing a seasonal employment schedule to accommodate fluctuating demand, optimizing the packing lab to quicken the sorting process, and installing new signage to help residents follow the flow of the facility.

The team designed a new workstation layout to increase consistency in inspector resource usage. They also devised a training program to increase consistency in an inspector's model rejection process, which included a switch to using micrometers to reduce variability while measuring.

“We really enjoy this experience. It’s very satisfying to see these students apply what they have learned and work on a project that is real life.”

Brent Kellum, Daikin Applied

“Being part of this project made me wish I had taken some of these classes in school! What impressed me most about the team was their knowledge and drive to bring better mathematical modeling to industry problems.”

Henry Wyneken, Syngenta SYNGENTA

DAIKIN APPLIED

Challenge

Challenge

Decrease the company's total amount of write-offs, such as spoiled crops, from 14% to at most 7% by using a methodical solution to determine optimal annual hybrid stocking values.

Create an accurate forecasting model that will allow the company to better plan the production cycles for their products and to reduce lead and storage times.

Outcomes

Outcomes

The team simulated customer purchasing decisions and developed a Newsvendor model to estimate the demand per hybrid and output a suggested stock level and inventory cover level for each hybrid. Through the implementation of the team's model, they estimated a potential reduction in write-offs from 14% to nearly 5%.

The team delivered a forecasting model that achieved 94% accuracy. To ensure the continued use of the model, the team provided Daikin with a user manual and a listing of jobs that were likely to become orders in the three months following the project's end.

“This is a good opportunity to engage with new engineers and faculty and to renew that desire and drive to creatively solve problems.”

Jon Stinson, Boston Scientific

Senior Design Projects | ISyE Magazine

22

Photo: Paul Udstrang

Chamberlain Gregg hypes up the crowd by performing a backflip on the turf of TCF Bank Stadium.

Double Duty Junior Chamberlain Gregg was named the 64th drum major in the history of the University of Minnesota marching band. When he isn’t leading the 320-member ensemble and hyping up the student section during Gopher game days, Gregg is developing his ISyE skills.

“Whether I’m creating something new or modifying something that already exists, I like the idea of slimming down a process,” Gregg told writer Olivia Hultgren in a story this fall for the College of Science and Engineering.

Gregg, who 3D prints and laser cuts in this free time, hopes to eventually apply those skills in a professional setting to visually represent business models.

Visit z.umn.edu/ISyE-Marching-Band to read more about how Gregg balances his drum roll duties with his ISyE coursework and interests in operations research.

23 Fall 2019 | Undergraduate Student Highlights

More Student Achievements Ten ISyE students received the first-ever Dr. Alan L. Eliason Undergraduate Achievement in ISyE Award. The awards were made possible by a generous donation from Dr. Eliason, who also presented the awards to each student at a ceremony in April.

Congratulations to students Rachel Eron, Ramsey Shaffer, Kristen Peterson, Matt Martell, Katie Riedel, Edward Kusiak, Hannah Schutz, Tongqing Chen, Katherine Azar, and Michaela Holmgren for the hard work and dedication that earned them this honor.

Senior Justin Tran, Junior Jagmehr Madan, and Sophomore Governess Simpson, whose intended major is Industrial and Systems Engineering, were all invited to the Forbes 30 Under 30 event in Detroit, Michigan. Each of them was selected to attend as a scholar, a prestigious honor that is bestowed upon only 1,000 students worldwide each year.

Senior Tongqing Chen presented her research “An Optimal Policy for an Assignment of Police Patrols in the UMPD Precincts” at the INFORMS Conference in Seattle, Washington in October.

GRADUATE STUDENT SPOTLIGHT

Facebook Finds Bright Minds at ISyE After

receiving

their

Ph.D.

another.

degrees in ISyE, Ruizhi Shi (left in photo) and Zeyang Wu moved to

Washington

state

to

launch

their careers at one of the largest technology companies in the world: Facebook.

At

conglomerate’s

the

social

Seattle

media campus,

Shi and Wu share the same job title—Research Scientist—yet their roles are starkly different from one

learning, is improving automation

Shi builds machine learning models to find ads that match individual

user

interests.

“My

Ph.D. background in ISyE helped me

land

my

job

at

Facebook,”

says Shi. “Courses like Probability Theory and Optimization gave me

a

deeper

machine

understanding

learning

models.”

of Wu,

who also specializes in machine

tools used by Facebook to remove harmful content. Says Wu: “My Ph.D. research was a fruitful journey in which I learned a lot about how to conduct complex research projects in a clear, precise, and actionable manner. I am grateful for the continuous and invaluable support from all the members of the ISyE department.”

Photo: Michael Hicks

Surgical Efficiency Ph.D. student Nate Witte’s summer internship at the Mayo Clinic’s Rochester headquarters kicked off what could become a multi-year research partnership. Witte, with the assistance of other Mayo researchers, was tasked with analyzing schedules for a surgical unit. His goal was to establish a new system that would allow for both flexible scheduling—between patient surgeries and surgeon roles or vacation time—and the need to limit Mayo’s physician and clinic queues.

“We looked at a large number of possible solutions,” says Witte, “including heuristic algorithms, look-up tables, and simulations. But after researching over a dozen of these options we decided on a dynamic programming approach using conditional probability.”

Employing this technique, Witte used Python to write an algorithm that connects to a tool he created in Excel.

Now, when Mayo’s staff make desired changes to a calendar using Witte’s tool, within 20 seconds they are shown the days with the longest expected queues, expected wait times, and other information. “It was great to have a tangible result that could help patients,” Witte says. “While it’s not in practice yet, we are hoping to implement it soon.”

25 Fall 2019 | Graduate Student Highlights

More Internship Experiences Ph.D. student Jiali Huang enjoyed her first-ever internship experience over the summer as a research scientist intern at Lyft’s San Francisco office. Over Huang’s three months on the job, her work was largely divided between two pricing and revenue management projects. The first project involved investigating rider incentives and developing optimization models to provide customized upselling offers for riders.

The second project had Huang applying machine learning algorithms to predict marginal cost and conversion probability of rides. She also built optimization models on top of the predictions to inform dynamic planned pricing decisions.

M.S. student Abdalla Osman spent ten weeks between June and August this summer as a Model Validation Quant intern for the Capital Model Validation team at US Bank’s Minneapolis offices. During his time at US Bank, Osman built a Small Business Credit Card Payment Rate Model using linear- and tree-based models. “The models I built improved upon the prediction error rate from about 5 percent of the Fractional Logistic Regression (their model) to 0.0002 percent,” says Osman.

Promoting ISyE in India In late August, Ph.D. student Jazeem Abdul Jaleel spoke to students within the I.E.S. College of Engineering in India about his experiences at the University of Minnesota and the opportunities available for Master’s and Ph.D. programs abroad. “I talked in brief about Industrial Engineering and the research conducted by our department [and] about my research in queueing systems,” Jaleel says. “The session was very interactive in nature.”

Photo: Junyu Yang

Training Undergraduates

Ph.D. student Junyu Zhang (back left) enjoyed a hiking trip with his Microsoft colleagues.

Machine Learning at Microsoft After joining Microsoft Research last year for a summer internship, Junyu Zhang returned to the company’s Redmond, Washington, headquarters for a second summer. During his three months as a research intern this year, Zhang worked in the Machine Learning and Optimization Group. His work was focused primarily on optimization theories that can have potential applications in machine learning—an area that is highly related to his own research in the ISyE Ph.D. program.

“This internship was a very interesting experience,” Zhang says. “Microsoft Research organized a lot of intern events ranging from hiking and sailing to museum trips. They also regularly invite top researchers from around the world to share their research results, which is a good brainstorming opportunity.”

Together with his Microsoft Research collaborators, Zhang completed two conference papers that were accepted by the International Conference on Machine Learning and the Conference on Neural Information Processing Systems.

In the interest of providing undergraduate students software and coding language support, the ISyE Department hired Ph.D. student Anthony Zhenhuan Zhang to be its first-ever Computational Software Teaching Assistant. As part of a monthly workshop series, Zhang opened the fall semester with a focus on R. “We are planning to [eventually] have workshops on other languages and softwares, such as Matlab and Python,” says Zhang. In the workshops, Zhang and students write functions, generate plots, and perform statistical tasks.

Presenting in Australia Ph.D. student Kang Kang spoke at the INFORMS Applied Probability Conference in Brisbane, Australia in July. The presentation covered findings from his recent research work on the effects that omni-channel ordering systems can have on coffee shops and other service systems. Fellow Ph.D. students Jazeem Abdul Jaleel and Alex Wickeham—who participated in the research project—joined Kang for the presentation.

Graduate Student Highlights | ISyE Magazine

26

Welcome to

Ying Cui Assistant Professor Ph.D., National University of Singapore, 2016 Joined: Spring 2020 Why did you become a professor of industrial engineering?

When I was young, I was very interested in mathematics. I became interested in industrial engineering during my Ph.D. studies as I found mathematics could be useful in real-world problems. I mostly worked on the theory, algorithms and emergent applications for mathematical programming.

My goal of research is to align statistics, operations research and machine learning with computational optimization.

What excites you about joining the University of Minnesota?

As a researcher, I think Minnesota is a good place for me not only because there are pioneers in the fields where I do research, but also because there are many good people in the departments of Mathematics, Statistics and Electrical and Computer Engineering that I can potentially work with for interdisciplinary research.

What value do you hope to provide your students?

With growth in technology, people call almost everything data science. I felt that this term has become overused. This motivates me to develop a course that is at the intersection of optimization, statistics, and computer science to give students a broad view of what data science is. This way, students can have a better background in the technology they need to know to find a job as a data scientist.

Krishnamurthy Iyer Associate Professor Ph.D., Stanford, 2012 Joined: Fall 2019 Why did you become a professor of industrial engineering?

In the beginning of my fourth year [as a mechanical engineering undergraduate] I took a course called Introduction to IEOR [Industrial Engineering and Operations Research]. That was the first time I ran into this idea of how you schedule jobs on machines, how do you study randomness and processes like Markov chains. I was very intrigued by this and became more curious. I took a bunch of different electives in that area.

What are your research interests?

My main research interests have been applications of stochastic modeling and game theory to the design and analysis of markets and service systems. In particular, I enjoy using tools from applied probability, queueing theory, as well as ideas from economics.

What value do you hope to provide your students?

I taught an introductory game theory course [at Cornell] to mainly juniors and seniors. Because it is an introduction to game theory, there’s no assumption of what they know. The course went through a very systematic development, from how you go about modeling a decision problem to studying auctions. The students [at Cornell] really loved the class. I hope to teach a similar course at ISyE.

27 Fall 2019 | New Faculty

Zhaosong Lu Professor Ph.D., Georgia Tech, 2005 Joined: Fall 2019 Why did you become a professor of industrial engineering?

My Bachelor’s and Master’s degrees were in applied math and I was very interested in optimization. Then I did some searching online and I found that most researchers in optimization actually affiliate with industrial and systems engineering departments.

What are your research interests?

I love to use my theoretical strengths to explore applications. I try to build a bridge between theory and practical applications. My main interests are optimization, machine learning, and computational statistics.

What excites you about joining ISyE at UMN? It’s an enormous university that has so many programs. It has everything. I think being at the University of Minnesota provides me with great opportunities to talk to people from different areas in different departments. That’s very exciting. Another thing: The ISyE Department has a weekly seminar where we invite people from other top schools to come here and give a talk. It’s good to meet with these people because otherwise you might not have the opportunity.

Yiling Zhang Assistant Professor Ph.D., University of Michigan, 2019 Joined: Fall 2019 Why did you become a professor of industrial engineering?

In my undergraduate study, I took an introductory course in operations research, which is an important track of industrial engineering. I found it very interesting and decided to explore it more. During my graduate studies, I met many excellent professors who inspired me. I wanted to become like them and encourage more students to go on a journey in this exciting field.

What are your research interests?

My research is mainly about how to make decisions in an uncertain environment. I am fascinated by challenging optimization problems from power systems, transportation, and healthcare operations. Most of my work is data-driven and application-motivated.

What value do you hope to provide your students?

I want to show my students the beauty of optimization through examples from application fields that impact our daily lives. Optimization can be a useful tool for the problems in their work, but it can also be a different philosophy they can use to tackle real-life problems.

New Faculty | ISyE Magazine

28

ALUMNI SPOTLIGHT

Amazon Eyes ISyE Alumni When Amazon needs to transport inventory between two warehouses, the online retail giant relies on employees like Tara Mardan (PhD 2006) to optimize the transaction. Mardan manages a team of research scientists who design pricing models for the company’s transportation department, ensuring that the “Middle Mile”— shipping goods between Amazon facilities—is both efficient and cost-effective. If a shipment needs

to move fast, Mardan’s job is developing models and algorithms that generate a price that’s attractive to drivers on the spot market—without overpaying. “It’s challenging but it brings a lot of satisfaction,” she says.

“You see the results of your model almost immediately.”

Mardan, a native of Iran, focused on supply-chain management during her Ph.D. in Minnesota. She then spent a decade at PROS, a leading revenue management solutions

provider, before moving to Amazon two years ago. Her teachers, advisors, and community in Minnesota all provided amazing support, she says, and her education helped boost her confidence as well as her career prospects. “Our professors helped prepare us for fresh challenges,” Mardan says. “It didn’t matter what the problem was, our brains were trained to look at the challenge, assess the resources, and then identify the solutions.”

ALUMNI NEWS & EVENTS

2018 ISyE Alumni Get-Together The second annual ISyE Alumni event attracted a fantastic crowd of energetic young professionals to Summit Brewing Company’s newly remodeled brewhouse in St. Paul. Everyone was in high spirits as they reminisced about their time in the ISyE Department and reconnected with old friends while making new ones.

About this event: Summit Brewing Co. October 4, 2018

ISyE Alumni Return to 56 Brewing for 2019 Reunion Inspired by all the great times they had during the first annual ISyE alumni gathering at 56 Brewing Company in 2017, students headed back to the Northeast Minneapolis brewery to reconnect once more.

With representation from both the graduate program and undergraduate program, this year’s ISyE alumni enjoyed live music and fresh brews at the annual alumni About this event: gathering. The Department 56 Brewing Co. of Industrial and Systems October 29 Engineering looks forward 5:30 to 7:00 PM to its next opportunity to reconnect with ISyE alumni.

Alumni News & Events | ISyE Magazine

30

ISyE professor Bill Cooper in his office.

First Penrose Award Goes to Bill Cooper Bill Cooper is known for being a thorough instructor. “If you ask him a question, he’ll start at the beginning of the problem.” says one ISyE student. Notes another: “He gets straight to the point.”

In the classroom, he regularly draws graphs by hand to explain complicated concepts.

Congratulations to...

The clarity of his teaching methods stands out to students— and Cooper’s skills recently earned him the first ever Russell

J. Penrose Excellence in Teaching Award.

Named after a CSE alumnus and donor, the Penrose Award recognizes Cooper’s talents as an instructor and educator, both in and outside the classroom.

Students who nominated Professor Cooper praised his effective and efficient teaching style, his ability to explain difficult concepts, and his approachability. “His personality makes him

Qie He for receiving research grants from the Argonne National Laboratory and the Metropolitan Council of Minnesota.

Kevin Leder for being elected as one of nine faculty members to represent the College of Science and Engineering in the Faculty and University Senates.

31 Fall 2019 | Department News and Awards

fun to talk to,” says one student.

“He also takes time to tell stories once in a while, which keeps things interesting.”

Cooper says he deeply appreciates the award. “I put a lot of thought and care into teaching and I like interacting with students,” he says. “So it’s nice to hear that people think I’m doing a good job.”

Diana Negoescu for being a finalist for the 2019 INFORMS Junior Faculty Interest Group (JFIG) Best Award and for the 2019 INFORMS Pierskalla Award.

Ankur Mani for receiving a multiyear grant from Transdev to study on demand transit.

Photo: Eric Miller

ISyE professor Lisa Miller (center) accepts her award from (left to right) former University of Minnesota president Eric Kaler , Regent Thomas Anderson, Provost Karen Hansen, and Alumni Association CEO Lisa Lewis.

Lisa Miller Honored for Undergraduate Teaching As Director of Undergraduate Studies, Lisa Miller has played a key role in mapping out ISyE’s foundational curriculum. But she also has a direct impact on students: She teaches several required courses for sophomores, juniors, and seniors. “I get to interact with every student who goes through our program,” Miller says. “I get to see them grow and develop, and being part of that process is very rewarding to me.”

Last

spring,

Miller

was

recognized with a Horace T. Morse-University of Minnesota Alumni Association Award for Outstanding Contributions to Undergraduate Education. In addition to honoring individual faculty members, the award, given out annually since 1965, contributes to the improvement of undergraduate education at the University by publicizing distinguished teaching.

Students who endorsed Miller during the nomination process

Yiling Zhang for being awarded an Honorable Mention in the 2019 INFORMS Optimization Society Student Paper Prize Competition.

Jiali Huang for being named a finalist for INFORMS’ 2019 Student Paper Prize in Revenue Management and Pricing.

described her as understanding, knowledgeable, and generous.

“Professor Miller shows up every day with a smile on her face and always treats her students with respect,” one observed. “She goes out of her way to inform students of new opportunities within the ISyE department and how we can use them to our benefit.

Miller says she was moved to receive the award: “This was a great honor and recognition.”

Saif Benjaafar for receiving an NSF grant to study the future of automated vehicles.

Behrooz Pourgannad for being selected as inaugural joint postdoctoral fellow with the Institute of Mathematics and its Applications and the Mayo Clinic.

Department News and Awards | ISyE Magazine

32

RECENT FACULTY PUBLICATIONS Anunrojwong, J., Iyer, K., and Lingenbrink, D. “Persuading Risk-Conscious Agents: A Geometric Approach,” The 15th Conference on Web and Internet Economics, 2019.

Benjaafar, S., G. Kong and X. Li. “Peer-to-peer Product Sharing: Implications for Ownership, Usage and Social Welfare in the Sharing Economy,” Management Science, 65, 477-493, 2019.

Benjaafar, S. and H. Ming. “Operations Management in the Age of the Sharing Economy: What is Old and What is New,” forthcoming in Manufacturing and Service Operations Management, 2019.

Chen, X., Lin, Q., and Wang, Z. ”Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization,” INFORMS Journal on Optimization, 2019.

Davarnia, D., Richard, J.-P., Içyüz-Ay, E., and Taslimi, B. “Network Models with Unsplittable Node Flows with Application to Unit Train Scheduling,” Operations Research, 67(4), 1053-1068, 2019.

He, Q., Irnich, S., and Song, Y. “Branch-and-Cutand-Price for Vehicle Routing Problems with Time Windows and Convex Node Costs,” Transportation Science, 53(5), 1409-1426, 2019.

Hu, M., Wang, Z., and Feng, Y. “Information Disclosure and Pricing Policies for Sales of Network Goods,” Operations Research, 2019.

Huang, J., Mani, A., and Wang, Z., Wang. “The Value of Price Discrimination in Large Random Networks,” 2019 ACM Conference on Economics and Computation, 243-244, 2019.

Kim, J., Tawarmalani, M., and Richard, J.-P. “On Cutting Planes for Cardinality-Constrained Linear Programs,” Mathematical Programming, Forthcoming.

Lingenbrink, D., and Iyer, K. “Optimal Signaling Mechanisms in Unobservable Queues,” Operations Research, 67(5), 1397-1416, 2019.

Liu, Y., Cooper, W. L., and Wang, Z. “Information Provision and Pricing in the Presence of Consumer

Search Costs,” Production and Operations Management, 28(7), 1603-1620, 2019.

Lu, Z. and Zhou, Z. “Nonmonotone Enhanced Proximal DC Algorithms for a Class of Structured Nonsmooth DC Programming,” forthcoming in SIAM Journal on Optimization, 2019.

Lu, Z., Zhou, Z., and Sun, Z. “Enhanced Proximal DC Algorithms with Extrapolation for a Class of Structured Nonsmooth DC Minimization,” Mathematical Programming, 176(1-2), 369-401, 2019.

Proestakis , A., Polizzi di Sorrentino, E., Brown, H. E., van Sluijs, E., Mani, A., Caldeira S., and Herrmann, B. “Network Interventions for Changing Physical Activity Behaviour in Preadolescents,” Nature Human Behaviour, 2, 778-787, 2018.

Storey, K., Leder, K., Hawkins-Daarud, A., Swanson, K., Ahmed, A., Rockne, R. C. and Foo, J. “Glioblastoma Recurrence and the Rold of O6-Methylguanine-DNA Methyltransferase Promoter Methylation” JCO Clinical Cancer Informatics, 2019.

Votke, S., Abdul Jaleel, J., Suresh, A., Delasay, M., Doroudi, S. and Gandhi, A. “Optimal Markovian Dynamic Control of Interference-Prone Server Farms,” 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2019.

Yang, P., Iyer, K., and Frazier, P. “Information Design in Spatial Resource Competition,” The 15th Conference on Web and Internet Economics, 2019.

Yuan, Y., Shen, X., Pan, W., and Wang, Z. “Constrained Likelihood for Reconstructing a Directed Acyclic Gaussian Graph,” Biometrika, 106(1), 109–125, 2019.

Zhang, Y., Dong, J., Kuruganti, T., Shen, S., & Xue, Y. “Distributionally Robust Building Load Control to Compensate Fluctuations in Solar Power Generation,” American Control Conference (ACC), 5857-586, 2019.

Zhang, Y., Lu, M. and Shen, S. “On the Values of Vehicle-to-Grid Electricity Selling in Electric Vehicle Sharing,” forthcoming in Manufacturing & Service Operations Management.

RECENT SEMINARS SPRING 2019

FALL 2019

February 27 Opher Baron

September 4 Andrew Lim

“Data Driven Forecasting and Revenue Management with Word of Mouth Dependent Reference Prices”

“Calibration of Robust Empirical Optimization Problems”

University of Toronto

March 6 Laurens Debo

Dartmouth College “Tipping in Service Systems: The role of a Social Norm”

March 27 Qi Zhang

University of Minnesota “A Process Systems Engineering Perspective on Adjustable Robust Optimization”

April 17 Martin Savelsbergh

Georgia Institute of Technology “Exploiting Decomposable Structure to Design Better Algorithms for Solving Integer Programs”

April 24 Xuanming Su

University of Pennsylvania “Pricing Models for Online Food Ordering Platforms: Commission Rates and Delivery Fees”

May 1 Pinar Keskinocak

Georgia Institute of Technology “Quantitative Models for Decision-Support in Healthcare Applications”

National University of Singapore

September 18 Sewoong Oh

University of Washington “The Power of Two Samples in Generative Adversarial Networks”

October 25 James Orlin

Massachusetts Institute of Technology “The Shortest Cycle Problem and the Second Shortest Path Problem”

October 30 Yun Fong Lim Singapore Management University

September 25 Jeff Kharoufeh

“Integrating Anticipative Replenishment-Allocation with Reactive Fulfillment for Online Retailing Using Robust Optimization”

“Staffing and Pricing in Co-Sourced Call Centers”

November 6 Abhishek Roy

Clemson University

October 2 Erik Erickson Hennepin County “Using Data and Analytics to Improve Human Resources”

Cargill

“CORTX—Optimization as a Service?”

November 13 Marc Meketon Oliver Wyman

October 9 Maged Dessouky

“Freight Railway Operations Research: A Look at the Most Important O.R. Applications in the Past 20 Years”

“Cost-Sharing Transportation Systems”

November 20 Kristopher Purens

October 16 Lavanya Marla

“Geospatial Big Data Analytics for Business and Conservation”

University of Southern California

University of Illinois Urbana-Champaign “Data-Driven Greedy Policies and Information-Relaxation Bounds for Ambulance Allocation and Dynamic Redeployment”

Descartes Labs

December 4 Michael Chmutov C.H. Robinson

“If You Could Send a Truck Anywhere, Where Would You Send It?”

December 11 Rhonda Righter

University of California, Berkeley “Service Systems with Server Compatibilities and Redundancy”

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