INFORMS 2021 Program Book

INFORMS Anaheim 2021

SB21

field trial deployments of GEMs in a variety of environmental contexts. The model incorporates realistic resource constraints and ecological data and is parameterized by laboratory-informed genetic inheritance patterns.

SB21 CC Room 204A In Person: Empirical Research in Emerging Services General Session Chair: Kejia Hu, Vanderbilt University, Nashville, TN, 37215-1710, United States 1 - Caring for an Aging Population in a Post-pandemic World: Emerging Trends in the U.S. Older Adult Care Industry Matthew Walsman, Rutgers Business School, Berkeley Heights, NJ, 07922-2110, United States, Lu Kong, Kejia Hu This paper examines older adult care services during the outbreak of the COVID- 19 global pandemic. We investigate emerging trends initiated or accelerated by the pandemic and predict their permanence in a post-pandemic world. We collected primary empirical data from both older adult care providing organizations (supply) and individuals receiving or considering care (demand) in the United States. We also collect qualitative data from various sources to supplement our quantitative surveys. 2 - Westore or Appstore: Customer Behavior Differences in Mobile Apps and Social Commerce Kejia Hu, Vanderbilt University, Nashville, TN, 37215-1710, United States, Nil Karacaoglu Social commerce is rapidly growing and attracting new customer segments. This channel is different from traditional retail channels in that it relies on peer-to- peer communication for product discovery in a social media platform. We examine customer behavior and search patterns in an emerging social commerce channel, namely the WeChat mini-programs, and in retailers’ native apps. We find that WeChat customers have higher fixed search costs yet smaller marginal search costs compared to App customers. Moreover, customer characteristics such as their user level and time of search impact search costs. We propose two channel-specific strategies that leverage customers’ search costs differences across channels. Both strategies can significantly increase conversion rate and profit for retailers. 3 - Trips for Tips? An Investigation of the Role of Passengertips in Drivers’ Relocation Decision-making Process Li Ding, Georgia Tech, Atlanta, GA, United States, Basak Kalkanci Using a large-scale and granular taxi trip dataset and structural estimation, we analyze the role of passenger tips in drivers’ relocation decision-making process. We show heterogeneous sensitivity to tips among new and experienced drivers, drivers below and above the income target. Through counterfactual analysis, we find that although increasing tip salience improves drivers’ income, it also reduces platform efficiency. SB22 CC Room 204B In Person: Optimization and Model-based Approaches to Genetic Screening and Genetic Decision Making General Session Chair: Kanix Wang, University of Chicago Booth School of Business, Chicago, IL, 60637-6877, United States 1 - Optimal Genetic Screening for Cystic Fibrosis Hussein El Hajj, Virginia Tech, Blacksburg, VA, 24061-1019, United States, Ebru Korular Bish, Douglas R. Bish Cystic fibrosis is among the most prevalent life-threatening genetic disorders. Early diagnosis improves quality of life and reduces healthcare expenditures. Most cystic fibrosis newborn screening processes start with a bio-marker test; followed by a genetic test, ending with diagnostic testing, which corrects false positives. On the other hand, a false negative represents a missed cystic fibrosis diagnosis. An important decision is which variants to include in the screening panel to reduce the false negative probability under a testing budget. We develop novel stochastic optimization models, and identify key structural properties of optimal panels, and use these properties to develop efficient algorithms. 2 - Enhancing Field Trials of Genetically Modified Organisms with Optimization Valeri Vasquez, Berkeley, Berkeley, CA, United States Optimization is crucial to defining effective deployment strategies for genetically engineered mosquitoes (GEMs). These transgenic organisms are designed for use as public health interventions; release details can be calibrated to save on implementation costs, to avoid the ecological consequences of excessive deployments, or to mitigate the potential epidemiological shortcomings of inadequate scheduling. I develop a nonlinear mathematical program to optimize

SB24 CC Room 205A In Person: Transformation of Urban Mobility and Its Implications General Session Chair: Hale Erkan, United States 1 - Toward Sustainable Cities: Bike Lane Planning with Endogenous Demand and Traffic Congestion Jingwei Zhang, University of California-Los Angeles, Los Angeles, CA, 90024-7212, United States, Sheng Liu, Auyon Siddiq, Keji Wei We study an urban bike lane planning problem considering endogenous demand and traffic congestion. Building bike lane attracts commuters to cycling and reduces traffic congestion, but narrows driving lanes and worsens traffic congestion. To investigate the net effect of bike lane construction on traffic congestion and improve cycling adoption, we formulate the network design problem as a bilevel programming problem. As model input, we structurally estimate travel time and mode choice model based on traffic equilibrium using data collected from multiple sources in downtown Chicago. As a result, we provide prescriptions on bike lane construction in the existing road network. 2 - Free Rides in Dockless, Electric Vehicle Sharing Systems Bobby Nyotta, UCLA Anderson School of Management, 25369 Avenida Ronada, Los Angeles, CA, 91355-3203, United States, Fernanda Bravo, Jacob Feldman We study free-ride policies as a mechanism to incentivize users of a dockless or free-floating electric vehicle sharing system (EVSS) to park vehicles at charging stations in order to maintain a charged fleet.We develop an infinite horizon dynamic program to analyze free-ride policies. We build on this initial formulation to construct a mixed-integer program that outputs intuitive, battery- threshold rules for when to offer free rides.In a discrete-event simulation model using real data from an EVSS, we compare the performance of this simple policy against other sophisticated policies, including the commonly used fine-based policy. Our simulation reveals this three-dimensional trade-off between customer satisfaction, revenue, and operational complexity. Our results are robust under many demand patterns and under a variety of network settings. SB25 CC Room 205B In Person: Sharing Economy and Green Technology/Volunteer Management Policies General Session Chair: Kamalini Ramdas, London Business School, London, NW1 4SA, United Kingdom 1 - Competitive Industry’s Response to Environmental Taxation for Green Technology Adoption Anton Ovchinnikov, Queen’s University, Smith School of Business, Kingston, ON, Canada, Dmitry Krass We consider market response to environmental taxes by firms producing a commodity good with a polluting by-product. The firms are asymmetric (heterogeneous) with respect to production efficiency and pollution control technology. Cournot (quantity) competition is assumed and two demand functions are considered: iso-elastic and linear. In this setting, two kinds of responses are considered: market response, where firms choose production quantities given their technology choices, and technology response, where firms also choose among a discrete set of available pollution abatement technologies. We are mainly interested in examining the limitations of using environmental taxes as a mechanism to incentivize “green” technology choice. 2 - Nonprofit Operations: Managing Volunteers and Paid Workers Lei Li, Purdue University, West Lafayette, IN, United States, Gemma Berenguer, William Haskell Nonprofit organizations run a workforce composed of a mix of volunteers, part- time workers, and full-time workers. We study this NPO’s staffing problem to determine the optimal initial staff planning and per period hiring and assignment decisions given uncertain supply of volunteers and part-time workers. Our goal is to study how to solve this problem in a way that is effective and easy to implement. We demonstrate that a prioritization assignment policy and a hire-up- to policy for part-time workers can be conveniently applied and are close to optimal. These policies are, in fact, optimal under staff scarcity and staff sufficiency. We further suggest two easy-to-implement heuristics and observe that both heuristics have low relative optimality gaps.

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