Informs Annual Meeting 2017

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INFORMS Houston – 2017

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2 - Seeking to Belong: How Beneficiaries Influence Performance Bradley R.Staats, University of North Carolina at Chapel Hill, Campus Box 3490, McColl 4721, Chapel Hill, NC, 27599-3490, United States, bstaats@unc.edu, Paul Green, Francesca Gino We examine how connecting to beneficiaries of one’s work increases performance. In a field experiment of harvesters, we find that beneficiary contact with the end customer did not improve productivity, contact with an internal beneficiary yielded a persistent increase in productivity. We validate this effect in the laboratory. 3 - Speed-quality Trade offs in Home Health: the Effects of Time Spent with Patients on Hospital Readmission Hummy Song, The Wharton School, University of Pennsylvania, 560 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA, 19104, United States, hummy@wharton.upenn.edu, Elena Andreyeva, Guy David Using a novel dataset on home health care episodes, this study quantifies the effects of reduced time spent with patients during a post-acute nursing visit on hospital readmissions. Since both visit length and readmission are likely to be correlated with unobserved illness severity, we use the daily sequence of nurse visits as an instrument for visit length. As the day progresses, visit length significantly shortens. Omitted variable bias leads to a positive correlation between visit length and readmissions. Using our instrument and controlling for patient, visit, and nurse characteristics, we examine how an increase in visit length affects the likelihood of hospital readmission. 4 - An Econometric Analysis of How Facility Layout Impacts Care Provision in the Emergency Department Lesley Meng, The Wharton School, University of Pennsylvania, 3730 Walnut Street, Suite 500 JMHH, Philadelphia, PA, 19104, United States, lmeng@wharton.upenn.edu, Robert Batt, Christian Terwiesch We study how the facility layout of ED patient rooms impacts nurse workflow decisions. Specifically, linking our infrared nurse location tracking data to room occupancy patient data, we are able to investigate potential facility layout variables, such as distance from the nurses’ station, that impacts the patient’s length of stay in the ED. 361B Behavioral Modelling for Critical Infrastructure and Security Issues Invited: InvitedBehavioral Aspects of OR Invited Session Chair: Ignacio J Martinez-Moyano, PhD, Argonne National Laboratory, Argonne National Laboratory, Argonne, IL, 60439, United States, imartinez@anl.gov Co-Chair: Todd E. Combs, Ph.D., Idaho National Laboratory, Idaho National Laboratory, 1955 N. Fremont Ave., Idaho Falls, ID, 83415, United States, todd.combs@inl.gov 1 - Optimizing Counter-propaganda Strategies to Prevent the Spread of Extreme Ideologies Online Chaitanya Kaligotla, PhD, Argonne National Laboratory, Chicago, IL, 60439, United States, ckaligotla@anl.gov This talk presents a mathematical model of opinion dynamics at an individual and group level, to characterize the spread of extreme ideologies on internet discussion platforms. Assuming the existence of some prevalent ‘red’ agents spreading extreme ideologies to other agents, an optimization problem is formulated to develop strategy for the delivery of counter-propaganda messages, by a group of ‘blue’ agents, to counter the spread of such extreme ideologies in a time and cost effective manner, a problem of increasing relevance to national security. This talk also extends existing research on opinion dynamics and behavioral modeling of internet users. 2 - Behavioral Considerations in Detection Dynamics: Interlacing Human Judgment, Outcome Decomposition, and Threshold Setting Ignacio J.Martinez-Moyano, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL, 60439, United States, imartinez@anl.gov Detection-selection mechanisms are at the core of security processes in complex systems. Security procedures are designed to protect systems from ill-intentioned individuals and other risks. Detection processes precede defensive action as the identification of threats is needed to determine adequate protective action. In order to identify threats in complex dynamic systems, the coupling of human judgment (many times aided by technology) and outcome understanding is essential to setting adequate thresholds for protective action. Understanding the interaction of human judgment, outcome decomposition, and threshold setting is a promising approach to improving security systems. SC49

361C Modeling and Analysis of Emerging Mobility Services I Sponsored: TSL, Urban Transportation Sponsored Session Chair: Yu Nie, Northwestern University, Evanston, IL, 60208, United States, y-nie@northwestern.edu 1 - Optimization-based Strategies for Autonomous Vehicle Fleet Operations Michael Hyland, Northwestern University, 600 Foster St, Evanston, IL, 60208, United States, mhyland@u.northwestern.edu, Hani S.Mahmassani We consider the problem of operating a fully autonomous vehicle fleet in real- time to serve passenger demand. The problem’s twin objectives include minimizing operational costs and maximizing quality of service. Given the highly dynamic and stochastic nature of the problem, we employ a rolling-horizon, optimization-based solution approach. We develop a simulation framework to model the dynamic, stochastic problem, and to test operational strategies. Lastly, we seek to generalize the computational results via empirical relationships between problem parameters (e.g. service area, average trip distance, etc.) and their impact on the problem’s objectives. 2 - Analyzing Regional Mobility Impact of Zero Occupancy Vehicles (ZOVs) in Presence of Connected and Automated Vehicles (CAVs) Omer Verbas, PhD, Argonne National Laboratory, Lemont, IL, 60439, United States, omer@northwestern.edu The deployment of CAVs in significant numbers is imminent. However, due to novelty of the technology, predicting their impact is challenging. In this study, we investigate the effect on regional mobility of ZOV travel enabled by CAVs. An optimization algorithm has been developed, to minimize the number of cars and travel cost of households, while optimizing individual daily activity-travel schedule, considering their simulated flexibility. The algorithm has been implemented in an advanced integrated activity-based travel demand simulator and scenarios were evaluated for the Chicago region. 3 - An Advanced Parking Navigation System for Downtown Parking Zhibin Chen, University of Michigan, Ann Arbor, MI, United States, chipin@umich.edu This paper develops a novel parking navigation system for downtown parking that aims to mitigate parking competition by guiding drivers to appropriate vacant parking spaces. Given drivers’ real-time locations and their parking preferences, a two-sided matching algorithm is adopted to achieve a stable driver-optimal matching, under which drivers will be guided to their most appropriate parking spaces (if any), and have no incentive to misreport their private information. Simulation experiments are conducted to demonstrate the ability of the proposed navigation system on reducing driving time and the times of changed assignment compared with other navigation systems. 4 - Fair Versus Optimum On-demand Ride-sharing Plan Jane Lin, University of Illinois-Chicago, 842 W. Taylor Street (M/C 246), Chicago, IL, 60607, United States, janelin@uic.edu, Luca Foti, Ouri Wolfson Ride sourcing companies such as Uber and Lyft are offering ride sharing service. When matching passengers, these services attempt to optimize savings at a global level. But it may result in a passenger A matched to passenger B, while if matched to passenger C both A and C would have saved more money. This introduces the concept of “fairness” in ride sharing, which consists of finding the Nash equilibrium in a ridesharing plan. We compare the optimum and the fair plan in different contexts. We show that the gap between the two is not significant when fares are computed on distance traveled and, as a result, the fair plan can be issued without significant losses. 5 - Taxi Market Competition under Spatial Equilibrium Zhoutong Jiang, Master Student, University of Illinois at Urbana-Champaign, Champaign, IL, Champaign, United States, zjiang30@illinois.edu The taxi market is complex to model when competition between different companies is considered. A Stackelberg game model based on continuum approximation is proposed to help taxi companies select best driver dispatching and pricing strategies under competition. Properties of market competition are demonstrated using numerical examples.

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