Informs Annual Meeting 2017

SC61

INFORMS Houston – 2017

4 - Reliable Sensor Deployment under Probabilistic Disruptions Siyang Xie, U. of Illinois at Urbana-Champaign, 205 N.Mathews Avenue, B156 Newmark Lab, Urbana, IL, 61801, United States, sxie13@illinois.edu, Zhoutong Jiang, Yanfeng Ouyang Many location-aware systems use multiple sensors for geographical positioning and surveillance. The accuracies depend on how the sensors are deployed and used, and may suffer from sensor disruptions when sensors are subject to failures. To optimize the sensor locations and maximize the positioning/surveillance accuracy under disruption risks, we first study the optimal layout of sensor locations on an infinite homogeneous plane. Then we develop a continuum approximation model to formulate the reliable sensor location problem under homogeneous/heterogeneous settings. Numerical examples are presented to illustrate how the proposed model can be applied for sensor deployment optimization. 5 - Predictive Models for Driver Aggressiveness on Arterial Streets using Unsupervised Learning Nabaruna Karmakar, Graduate Research Assistant, NC State University, Raleigh, NC, United States, nkarmak@ncsu.edu, Seyedbehzad Aghdashi, Masoureh Jeihani, Celeste Chavis, Zohreh Rashidi Moghaddam Predictive models have been developed to determine drivers’ aggressiveness in response to change in arterial road Level of Service (LOS) through data mining of large amount of trajectory and probe vehicle data. K-means clustering has been used to recognize patterns in trajectory data. A Neural Network model is proposed to generate predictive models. Models developed in this research will help decision makers to determine safety impacts of arterial streets treatments. Chair: Steven Harrod, PhD, Technical University of Denmark, Bygning 424, Kgs. Lyngby, 2800, Denmark, stehar@dtu.dk 1 - Awards and Three Speakers to be Announced at Conference Steven Harrod, Technical University of Denmark, Room 132, Building 424, Kgs. Lyngby, 2800, Denmark, stehar@dtu.dk Awards for the 2017 Railway Applications Section Student Paper Competitions will be presented, along with presentations by the finalists. Winners are announced at the conference, or a few weeks before at the earliest. 370A Analytics Curricula in OR/IE Programs Sponsored: INFORMEd Sponsored Session Chair: Joel Sokol, Georgia Institute of Technology, Georgia Institute of Technology, Atlanta, GA, 30332-0205, United States, jsokol@isye.gatech.edu 1 - MS in Data Science and MS in Business Analytics at ColumbiaUniversity Adam Elmachtoub, Columbia University, Columbia University, New York, NY, 10027, United States, adam@ieor.columbia.edu, Garud N.Iyengar The MS in Data Science is offered by the Data Science Institute at Columbia University. Our networked world is generating a extremely large amounts of data that has the potential to transform business, government, engineering and the applied sciences. A new discipline of data science is focused on the tools required to generate information from this data. This MS program is focused on developing SC60 SC59 362F Joint session RAS/Practice: RAS Student Paper Awards and Presentations Sponsored: Railway Applications Sponsored Session

3 - The Cornell M.Eng. Program – Data Analytics and Beyond David B. Shmoys, Cornell University, 231 Rhodes Hall, 136 Hoy Road, Ithaca, NY, 14853-3801, United States, david.shmoys@cornell.edu The Cornell M.Eng. Operations Research program provides a number of concentrations, & the fastest growing is in Data Analytics, blending coursework in OR, data science, & computing, with a client-based project where a team of students work with a faculty member to address a real-world problem. This is complemented by a new program at Cornell Tech in NYC, which combines traditional academic rigor with the energy & opportunity that come with studying in the heart of NYC’s tech startup community, & includes an immersive Studio experience, that develops team-building and leadership skills as well as new product ideas in response to the strategic needs of a real organization, & create your own startup. 4 - Georgia Tech’s Interdisciplinary MS inAnalytics: On-campus, Online, and International Joel Sokol, Director, Master of Science in Analytics, Georgia Institute of Technology, School of ISYE, 765 Ferst Drive, Atlanta, GA, 30332-0205, United States, jsokol@isye.gatech.edu Georgia Tech’s Master of Science in Analytics degree is co-owned and co- administered by the College of Engineering, College of Computing, and Scheller College of Business. With an interdisciplinary core, three tracks, 60+ elective choices, and an applied practicum requirement, the degree allows students to specialize and personalize their learning to fit their individual interests and career goals. We offer a premium on-campus option with a variety of perqs, a high- quality MOOC-style online delivery, and a combined MS/MBA option. In this talk, we’ll show how it all works. Response Logistics Sponsored: Minority Issues Sponsored Session Chair: Emmett J Lodree, University of Alabama, Tuscaloosa, AL, 35487- 0226, United States, ejlodree@cba.ua.edu 1 - Managing Volunteer Convergence at Disaster Relief Centers Disaster Relief center managers face uncertainty in both the timing and quantity of donations, as well as the number of spontaneous volunteers. Volunteer convergence is one of the biggest challenges for relief center managers. To better understand this phenomenon and find effective management strategies, we develop an agent-based simulation model consisting of donors providing relief items, beneficiaries in need of relief items and random arrivals and departures of volunteers. We investigate volunteer assignment policies that reduce donor beneficiary waiting time under given changes in volunteer capacity, available inventory, and beneficiary’s arrival rates. 2 - The Optimal Assignment of Spontaneous Volunteers to Parallel Queues with Stochastic Demand Kyle Paret, North Carolina State University, Raleigh, NC, United States, keparet@ncsu.edu, Maria Esther Mayorga Immediately following a disaster people converge to assist the affected community. Spontaneous volunteers (SVs) are in a unique position to provide valuable aid at a crucial point in the disaster cycle. Oftentimes these volunteers are ineffectively used or refused all together. Improved methods to integrate spontaneous volunteers are required. In this paper, a multi-server queuing model is formulated to represent the dynamics of assigning SVs to tasks in a post-disaster setting. An optimal assignment policy is generated using a Markov Decision Process. Finally, we use simulation to compare the optimal policy against several heuristic policies and discuss real world implications. 3 - Ambulance Service Region Design under Uncertainty Shakiba Enayati, State University of New York, Plattsburgh, NY, United States, senayat@ncsu.edu, Osman Ozaltin, Maria Esther Mayorga Limiting response area for each ambulance may lead to shorter response times to emergency scenes and more evenly distributed workload for ambulances. We propose a two-stage stochastic mixed-integer programming model to address the service region design problem under uncertainty. The output of this model is to locate ambulances at the potential stations in the service area and to assign a set of demand zones to each station at different backup levels. The objective function of our model is to maximize the expected number of covered calls while the assigned workload to each ambulance is restricted. The proposed model can be optimized offline similar to the idea of “patrol-beats” used in policing models. Hussain Abualkhair, North Carolina A&T.State University, Greensboro, NC, United States, hfabualk@aggies.ncat.edu, Lauren Davis SC61 370B Stochastic Models for Emergency

new methodologies and applying them to innovative applications. 2 - Master of Science in Analytics at Northwestern: The Importance of Agility

Barry L. Nelson, Northwestern University, Dept of Industrial Eng and Mgmt Sciences, 2145 Sheridan Road, Room C216, Evanston, IL, 60208-3119, United States, nelsonb@northwestern.edu, Diego Klabjan Analytics is a quickly evolving field and thus education programs must be adaptable and agile. We will discuss how the Master of Science in Analytics program at Northwestern University copes with these challenges.

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