INFORMS 2021 Program Book

INFORMS Anaheim 2021

TB41

“2 - Training Analytics Students to Work in Cloud Computing Environments Cody Baldwin, Director (M S. of Business Analytics), University of Wisconsin-Madison, Madison, WI, United States Many employers are now demanding that recent graduates in business analytics be comfortable working in cloud computing environments. (As an example, students should be able to load, transform, and connect to data in a cloud data warehouse.) University programs that neglect this fact will leave their students at a disadvantage going into the job market. This paper outlines work at the University of Wisconsin-Madison to address this need within their Master of Business Analytics program and includes proposed learning objectives and lessons. In Person Committee’s Choice Committee Choice: Committee’s Choice Committee Choice Session Chair: David Morton, Northwestern University, Evanston, IL, 60208- 0834, United States Co-Chair: Giacomo Nannicini, IBM T.J. Watson, Yorktown Heights, NY, 10598, United States 1 - Can’t Wait: Reducing Treatment Delay for Psychiatric Patients Nathan Adeyemi, Northeastern University, Boston, MA, United States, Nasibeh Zanjirani Farahani, Amanda Graham, Kalyan Pasupathy, Kayse Lee Maass Hospital emergency departments (ED) are often heavily backlogged by patients in need of care but awaiting placement in an inpatient bed (IP) either at their current hospital or transfer to another facility. This is known as ED boarding and disproportionately affects patients requiring psychiatric care and to a greater extent, its subpopulation of pediatric patients. Our goal is to find novel modifications for the current system that are effective in reducing ED boarding due to lack of available IP beds, distance-related transfer restrictions, and patient- characteristic related inclusion and exclusion criteria that minimize disparities by age and geographic region. 2 - Design of Staged Alert Systems for COVID-19 David Morton, Northwestern University, Evanston, IL, 60208- 0834, United States, Nazlican Arslan, Daniel Duque, Bismark Singh, Ozge Surer, Haoxiang Yang, Lauren Meyers Judicious implementation and relaxation of pandemic restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. We describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in Austin, Texas through the COVID-19 pandemic, acknowledging inequities, and accounting for an exit strategy under effective vaccines. 3 - Simpler (classical) and Faster (quantum) Algorithms for Gibbs Partition Functions Giacomo Nannicini, IBM T.J. Watson, Yorktown Heights, NY, 10598, United States, Srinivasan Arunachalam, Vojtech Havlicek, Kristan Temme, Pawel Wocjan We consider the problem of approximating the partition function of a classical Hamiltonian using simulated annealing. This requires the computation of a cooling schedule, and the ability to estimate the mean of the Gibbs distributions at the corresponding inverse temperatures. We propose classical and quantum algorithms for these two tasks, achieving two goals: (i) we simplify the seminal work of Štefankovi , Vempala and Vigoda (J. ACM, 56(3), 2009), improving their running time and almost matching that of the current classical state of the art; (ii) we quantize our new simple algorithm, improving upon the best known algorithm for computing partition functions of many problems, due to Harrow and Wei (SODA 2020). A key ingredient of our method is the paired-product estimator of Huber (Ann. Appl. Probab., 25(2),2015). TB44 CC Room 213B

TB41 CC Room 212A In Person: Power System Resilient Design and Optimization General Session Chair: Beheshteh Raouf, Clarkson University, Potsdam, NY, United States Co-Chair: Seyedamirabbas Mousavian, 1 - Load Frequency Control of Interconnected Power System Based On Kharitonov’s Theorem Beheshteh Raouf, Clarkson University, Potsdam, NY, United States Frequency deviations from the acceptable range can hurt the system’s stability and reliability. These deviations may even cause the power grid to collapse. So, frequency control is one of the major concerns of the power grids operators. Many papers studied the frequency control requirements and optimization techniques. In this study, we apply Kharitonov’s theorem to tune the PI controller parameters of a two-area power system in the presence of EV fleets. Each area consists of EV fleets, thermal, gas, and hydro units. We conduct the primary stability of the system by the zero-inclusion principle and examination of the Routh-Hurwitz stability criterion. After tuning the gains of the PI controller, we analyze the performance of the proposed method based on an attack on the communication of EV fleets, variation in system parameters, and load variation. 2 - Learning-based Predictive Control via Real-time Aggregate Flexibility Tongxin Li, California Institute of Technology, Pasadena, CA, 91125, United States, Yue Chen, Bo Sun, Adam Wierman, Steven Low Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads with a system operator via aggregate flexibility. However, most of existing aggregate flexibility measures often are slow-timescale and much less attention has been paid to real-time coordination. In this presentation, we consider solving an online optimization in a closed-loop system and present a design of real-time aggregate flexibility. Combining learning and control, we show that the feedback can be approximated using reinforcement learning and used as a penalty term in a novel control algorithm the penalized predictive control (PPC). We show that under certain regularity assumptions, the PPC is optimal. We illustrate its efficacy for electric vehicle charging networks and show that PPC outperforms the classical MPC. 3 - An Iterative Approach to Finding Global Solutions of AC Optimal Power Flow Problems Ling Zhang, University of Washington, Seattle, WA, 98119, United States, Baosen Zhang The existence of multiple solutions to AC optimal power flow (ACOPF) problems has been noted for decades. Existing solvers are generally successful in finding local solutions, which are stationary points but may not be globally optimal. In this paper, we propose a simple iterative approach to find globally optimal solutions to ACOPF problems. First, we call an existing solver for the ACOPF problem. From the solution and the associated dual variables, we form a partial Lagrangian. Then we optimize this partial Lagrangian and use its solution as a warm start to call the solver again for the ACOPF problem. By repeating this process, we can iteratively improve the solution quality, moving from local solutions to global ones. The simulation results show that our algorithm can escape from local solutions to achieve global optimums within a few iterations. TB43 CC Room 213A In Person: Education Contributed Session Chair: Cody Baldwin, Brigham Young University-Hawaii, Hauula, HI, 96717, United States 1 - Key Performance Indicators in Virtual Education Systems for Adolescent Students with Attention-deficit/hyperactivity Disorder During the Covid-19 Era Janet Choi, University of Southern California, Los Angeles, CA, United States “Kx” Due to the COVID-19 pandemic, public health guidelines displaced students from in-person learning conditions to online and socially distant platforms. The study explores this newly adapted format, namely virtual education, and its role on individuals vulnerable to these transitions, specifically students diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). The goal is to identify the academic performance indicators from virtual education that challenge adolescent students with ADHD and evaluate the symptom amplification. The study inquired more indicators with inattentive type ADHD over hyperactive type ADHD, implying that virtual systems likely incite inattentiveness.

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