Pitt Industrial Engineering Students Apply Their Knowledge in a Collaboration with Grane Rx

Industrial, Student Profiles

PITTSBURGH (February 4, 2019) … A group of University of Pittsburgh industrial engineering undergraduate students spent the fall 2018 semester helping a local pharmaceutical supplier balance production and optimize distribution strategies. The work was part of a Swanson School of Engineering senior capstone project, a program that allows students to gain valuable industry experience with local companies while pursuing their degrees. “We work with a diverse set of industry partners around Pittsburgh to identify problems that take advantage of the range of skills learned in class, the industrial experiences the students have from internships and cooperative engineering programs, and the experiences of our faculty,” said Louis Luangkesorn, assistant professor of industrial engineering and coordinator of the department’s capstone program. “The project puts the students in a setting where they have to work with the customer to identify the underlying problem and develop a solution within a limited time frame.” The group of undergraduates worked with Grane Rx, a pharmaceutical supplier for Programs of All-Inclusive Care for the Elderly (PACE),  skilled nursing centers, and personal care homes in multiple states on the East Coast. The company is planning an expansion of their PACE Pharmacy services to the West Coast with a significant amount of new participants expected in a short period of time. To help manage this growth, Grane Rx recruited the help of Pitt IE students and faculty to strategize ways to meet production and distribution needs. The students’ first goal was to create a working production scheduling model that optimizes weekly and daily production and allows for business growth. The second goal was to provide a weekly production cost analysis that compares the options for overtime production once the new Colorado facility reaches its capacity. “We created both models by having meetings with the Grane Rx resources, analyzing data sets provided by the company, holding group design sessions, and coding in VBA and Matlab,” said Julie Shields, who recently graduated with a bachelor’s degree in industrial engineering. “The project helped improve our coding and project management skills, both of which may be useful in our future careers.” As part of the capstone, students created weekly progress reports and met with Grane Rx employees along with University of Pittsburgh faculty who served as mentors and advisors. Quintin Graciano, an operations project manager who helped supervise the group, said, “The production model created by the students provided Grane Rx a fresh and unique view of our new PACE production processes. We have incorporated several new production tools at our Denver PACE pharmacy. The students were engaging and committed to providing a tool that made a difference.  Mission accomplished!” According to Shields, the most important skill that the group gained was effective delegation based on the talents of each team member. She said, “Being able to improve these skills and gain meaningful industry experience before we graduate was extremely valuable.” The team presented their project at the Swanson School of Engineering’s Fall 2018 Design Expo where they took first place in the industrial engineering category. Dr. Luangkesorn said, “The work with Grane Rx provided a good example of a project that showcased the abilities of our partner and our students, enhancing the students’ project management and technical skills while helping local industry grow.” ### About Grane Rx For nearly 25 years, Grane Rx has been a leader in pharmacy solutions and services for PACE organizations and post-acute care providers across the United States. Our customer centric pharmacy approach optimizes Care Center operations so providers can deliver the most seamless, accurate and convenient pharmacy experiences to their patients and participants. Our PACE Pharmacy Solutions include Meds2Home packaging, EasyRead Pharmacy labels, and LearnRx literacy tools available in 22 different languages, which are designed to revolutionize pharmacy services and outcomes. Grane Rx leverages senior care pharmacy experts and the newest technologies to provide universal, best-in-class service to patients, participants and Centers alike. For more information, contact Scott Sosso at ssosso@GraneRx.com or call 412-449-0504 or visit www.GraneRx.com.

IE Undergraduate Julie Shields Named Swanson School’s 2018 Co-op Student of the Year

All SSoE News, Industrial, Student Profiles

PITTSBURGH (November 14, 2018) … The University of Pittsburgh Swanson School of Engineering Cooperative Education Program named Julie Shields, a senior industrial engineering student, its Co-op Student of the Year. Shields worked three, four-month rotations with FedEx Supply Chain. “I started in a standard engineering role where I designed over one million square feet in warehouse layouts, and I later became a sales representative where I presented the layouts to potential customers,” said Shields. “Each experience with FedEx Supply Chain has allowed me to grow my technical, professional, and personal skills.” “During Julie’s third rotation, she was lead engineer on a complex $350 million bid, which the company ultimately won,” said Maureen Barcic, Cooperative Education Director at Pitt. “Outside of her co-op experience, Julie is also a member of Tau Beta Pi, Students Consulting for Non-Profit Organizations, Alpha Pi Mu, IIE, and was the Co-Chair for the 2017 Freshman Engineering Conference. She holds a 3.9 GPA and has been awarded two national scholarships.” Shields will utilize the skills that she gained from her co-op experience as she starts full time with FedEx Supply Chain in January. The other finalist was Xavier Strittmatter, a chemical engineering student who worked for Johnson & Johnson/McNeil Healthcare. Cole Burden, a civil engineering student, was awarded the Co-op Tenacity Award for his work at American Bridge. Honorable mentions include: Robyn Moyer, Electrical Engineering, ZOLL LifeVest Julia Marzocca, Mechanical Engineering, Nissan Sean O’Brien, Mechanical Engineering, Nissan Logan Higinbotham, Computer Science, Ansys Jessica Rodgers, Mechanical Engineering, FedEx Ground Casey Kinol, Chemical Engineering, Lubrizol Abigail Wezelis, Computer Engineering, ANSYS Jenna Rudolph, Mechanical Engineering, Cleaveland/Price, Inc. Rushil Shah, Industrial Engineering, The Bank of New York Mellon Casey McBride, Bioengineering, Mine Safety Appliances The 2018 Co-op Employer of the Year is Johnson & Johnson Consumer Inc. “We have had a great relationship with the Consumer Healthcare Division of J&J for many years,” said Barcic. “They have hired over 60 co-ops in the Fort Washington, Lancaster, and Lititz, Pennsylvania locations and have given tremendous opportunities to many of our students. They have also hired a number of these individuals on a full-time basis.” Other employers who were nominated include: Connors Group FedEx Ground  Philips Human Engineering Research Lab (HERL) About the Cooperative Education ProgramThe University of Pittsburgh Swanson School of Engineering Cooperative Education Program was reestablished in 1987 with the initial goal of 20 percent participation among undergraduate students. By 2018, almost 50 percent of undergraduates at the Swanson School participate in the co-op program. More than 6,500 students have participated over the years, with more than 80 percent completing all three required rotations. ###

Pitt Researcher Uses Video Games to Unlock New Levels of A.I.

Industrial

PITTSBURGH (November 5, 2018) … Expectations for artificial intelligences are very real and very high. An analysis in Forbes projects revenues from A.I. will skyrocket from $1.62 billion in 2018 to $31.2 billion in 2025. The report also included a survey revealing 84 percent of enterprises believe investing in A.I. will lead to competitive advantages.“It is exciting to see the tremendous successes and progress made in recent years,” says Daniel Jiang, assistant professor of industrial engineering at the University of Pittsburgh Swanson School of Engineering. “To continue this trend, we are looking to develop more sophisticated methods for algorithms to learn strategies for optimal decision making.” Dr. Jiang designs algorithms that learn decision strategies in complex and uncertain environments. By testing algorithms in simulated environments, they can learn from their mistakes while discovering and reinforcing strategies for success. To perfect this process, Dr. Jiang and many researchers in his field require simulations that mirror the real world.“As industrial engineers, we typically work on problems with an operational focus. For example, transportation, logistics and supply chains, energy systems and health care are several important areas,” he says. “All of those problems are high-stakes operations with real-world consequences. They don’t make the best environments for trying out experimental technologies, especially when many of our algorithms can be thought of as clever ways of repeated ‘trial and error’ over all possible actions.”One strategy for preparing advanced A.I. to take on real-world scenarios and complications is to use historical data. For instance, algorithms could run through decades’ worth of data to find which decisions were effective and which led to less than optimal results. However, researchers have found it difficult to test algorithms that are designed to learn adaptive behaviors using only data from the past.Dr. Jiang explains, “Historical data can be a problem because people’s actions fix the consequences and don’t present alternative possibilities. In other words, it is difficult for an algorithm to ask the question ‘how would things be different if I chose door B instead of door A?’ In historical data, all we can see are the consequences of door A.”Video games, as an alternative, offer rich testing environments full of complex decision making without the dangers of putting an immature A.I. fully in charge. Unlike the real world, they provide a safe way for an algorithm to learn from its mistakes.“Video game designers aren’t building games with the goal to test models or simulations,” Dr. Jiang says. “They’re often designing games with a two-fold mission: to create environments that mimic the real world and to challenge players to make difficult decisions. These goals happen to align with what we are looking for as well. Also, games are much faster. In a few hours of real time, we can evaluate the results of hundreds of thousands of gameplay decisions.”To test his algorithm, Dr. Jiang used a genre of video games called Multiplayer Online Battle Arena or MOBA. Games such as League of Legends or Heroes of the Storm are popular MOBAs in which players control one of several “hero” characters and try to destroy opponents’ bases while protecting their own. A successful algorithm for training a gameplay A.I. must overcome several challenges, such as real-time decision making and long decision horizons—a mathematical term for when the consequences of some decisions are not known until much later.“We designed the algorithm to evaluate 41 pieces of information and then output one of 22 different actions, including movement, attacks and special moves,” says Dr. Jiang. “We compared different training methods against one another. The most successful player used a method called Monte Carlo tree search to generate data, which is then fed into a neural network.”Monte Carlo tree search is a strategy for decision making in which the player moves randomly through a simulation or a video game. The algorithm then analyzes the game results to give more weight to more successful actions. Over time and multiple iterations of the game, the more successful actions persist, and the player becomes better at winning the game.“Our research also gave some theoretical results to show that Monte Carlo tree search is an effective strategy for training an agent to succeed at making difficult decisions in real-time, even when operating in an uncertain world,” Dr. Jiang explains. Dr. Jiang published his research in a paper co-authored with Emmanuel Ekwedike and Han Liu and presented the results at the 2018 International Conference on Machine Learning in Stockholm, Sweden this past summer. At the University of Pittsburgh, he continues to work in the area of sequential decision making with Ph.D. students Yijia Wang and Ibrahim El-Shar. The team focuses on problems related to ride-sharing, energy markets, and public health. As industries prepare to put A.I. in charge of critical responsibilities, Dr. Jiang ensures the underlying algorithms stay at the top of their game. ###
Matt Cichowicz, Communications Writer