Informs Annual Meeting 2017

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INFORMS Houston – 2017

2 - Social Sharing and Emerging Infectious Disease Contexts Bridget Kelley, RTI International, Research Triangle Park, NC, United States, Sarah Ray, Brian Southwell, Linda Squiers, Molly Lynch Social media may amplify information sharing, but their role during infectious disease outbreaks is unclear empirically. Our content analysis of Twitter posts (n=400) during the 2014 Ebola outbreak suggests most (61%) shared news articles whereas almost none shared government or public health websites, which holds implications for communication efforts. 3 - Brainwave Data Analytics Kyla McMullen, University of Florida, Gainesville, FL, United States, dr.kyla.mcmullen@gmail.com Brain-computer interface (BCI) involves the collaboration between a brain and a hardware device, which enables signals from the brain to direct some external activity. BCI data contains the recordings of brain activity through a number of channels over time. Using an Electroencephalography (EEG) device to record brainwave data, a channel can be defined as the electrical potential between a recording electrode and a reference electrode, on the scalp. To control a BCI, the user must produce different brain activity patterns that are identified by a system and translated into commands. The present work uses pattern recognition to create an adaptive brainwave classification system for contexts in which portions of the brainwave signals are missing or otherwise deteriorated. 4 - Does Government Surveillance Give Twitter the Chills? Uttara Ananthakrishnan, Carnegie Mellon University, 1 Bayard Road, # 28, Pittsburgh, PA, 15213, United States, uttara@cmu.edu Since Edward Snowden’s revelations regarding mass surveillance programs implemented by the NSA, the research community has attempted to estimate what many refer to as “chilling effects” of surveillance, or the tendency to self- censor in order to cope with mass monitoring systems. We propose a new method in order to test for chilling effects in online social media platforms. We use a unique, large dataset of Tweets and propose the use of new statistical machine learning techniques in order to detect anomalous trends in user behavior (use of predetermined, sensitive sets of keywords) after Snowden’s revelations made users aware of existing surveillance programs.

2 - A Real-time Drone Reroute Planning under Uncertain Flight Duration Seonjin Kim, University of Houston, 529 Barker Clodine, 17101, Houston, TX, 77094, United States, sonjin64@gmail.com, Gino J.Lim A real-time drone reroute planning method is proposed to handle the issue of uncertain flight duration. Such undesired situations can arise due to unexpected external events such as strong winds and moving obstacles on the flight path. A rerouting algorithm is designed to utilize alternative routes in real-time. A solution approach is also developed to improve the computational performance of the proposed optimization model. In a numerical example, feasibility and effectiveness of the proposed method are illustrated using a case study. 3 - Strategic Network Design for Parcel Delivery with Drones Fatma Gzara, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L.3G1, Canada, fgzara@uwaterloo.ca We study the effects of drone delivery on network design for e-retailers. We develop a nonlinear optimization model that locates warehouses and decides on services offered where one of the services is delivery by drones within a short time window. Customer demand is modeled using interaction model. We develop a novel Logic based Benders decomposition algorithm to solve the nonlinear formulation and perform numerical testing on the NYC case. 4 - Utilization of Unmanned Aerial Vehicles in Port Operations Jaeyoung Cho, Lamar University, P.O. Box 10024, Beaumont, TX, 77710, United States, jcho@lamar.edu, Maryam Hamidi, Berna Tokgoz The purpose of this study is to lay the foundation for the development of future port system using unmanned aerial system. We propose a new mathematical optimization model of chemical tankers routing which are performing their respective loading / unloading operations by the support of unmanned aerial vehicles (UAVs). This problem is modeled as a two-echelon vehicle routing problem as the route of the vessels and the path of UAVs must be optimized at the same time. 351C AAS Best Student Presentation Competition II Sponsored: Aviation Applications Sponsored Session Chair: Virginie Lurkin, Ecole Polytechnique Fédérale de Lausanne, Route Cantonale, Lausanne, 1015, Switzerland, vlurkin@ulg.ac.be Co-Chair: Peng Wei, Iowa State University, Iowa State University, Ames, IA, 50011, United States, pwei@iastate.edu 1 - A Static Model in Single Leg Flight Airline Revenue Management Behrooz Pourghannad, University of Minnesota, 1007 29th Avenue SE, Apt B, Minneapolis, MN, 55414, United States, behrooz@umn.edu Static models on single leg airline revenue management generally consider booking limits or protection limits as the main decision variables to control reservation requests. In the current paper, we provide an alternative framework in which the decision variables are the closing times of fare classes. In a continuous time model with nonhomogeneous Poisson arrivals, cancellations, and noshows, we study the problem of finding optimal closing times to maximize the expected net revenue from a given flight. We analyze the value function, point out some easy cases, and bring an easily implementable dynamic programming based solution method. We also illustrate this method on some numerical examples. 2 - An Optimization Approach for Airport Slot Allocation under the IATA Guidelines Nuno Ribeiro, cmu, Portugal, Coimbra, Portugal, nribeiro@andrew.cmu.edu We present a novel modeling and computational approach to optimize slot allocation decisions at schedule-coordinated airports. The model minimizes the displacement from airlines’ slot requests, while complying with the “primary criteria” of the IATA guidelines and airport declared capacities. We introduce a strong formulation that provides exact solutions in short computation times at medium-size airports for a full season of operations. Results from Madeira (FNC) and Porto (OPO) airports suggest that the model can improve the slot allocation outcomes, as compared to historical decisions. SC33

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351A Industry Job Search Panel Invited: INFORMS Career Center Invited Session Chair: Warren Hearnes, Cardlytics, Atlanta, GA, whearnes@hotmail.com

This panel discusses the industry interview process and do’s and don’ts associated with the job search. In addition to comment from current and former recruiters, time will be provided for questions and answers.

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351B Drone Delivery Systems – III Invited: InvitedDrone Delivery Systems (tentative title) Invited Session Chair: Jaeyoung Cho, Lamar University, Beaumont, TX, 77710, United States, jcho@lamar.edu 1 - Optimizing a Drone Network to Deliver Automated External Defibrillators Justin James Boutilier, University of Toronto, 532 Palmerston Boulevard, Apartment 6, Toronto, ON, M6G 2P5, Canada, j.boutilier@mail.utoronto.ca, Timothy Chan Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest (OHCA), but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. In this paper, we develop a framework that combines both location and queueing models to optimize the deployment of drone resources. In particular, we use a CVaR-based response time model that accounts for the historical 911 response time distribution and models the efficiency-equity trade-off between urban and rural areas. We applied our framework to 50,000 historical OHCAs in a large area composed of rural and urban regions surrounding Toronto, Canada.

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