2015 Informs Annual Meeting

MC02

INFORMS Philadelphia – 2015

78 - Using Past Scores and Regularization to Create a Winning NFL Betting Model Eric Webb, Graduate Student, Indiana University, 1309 E. 10th Street, Bloomington, IN, 47405, United States of America, ermwebb@indiana.edu, Wayne Winston Is the National Football League betting market efficient? We have devised a profitable betting model that would win 52.9% of the 7,554 bets against the spread it would have made over 33 seasons. Scores from previous weeks are used to estimate the point value of each team’s offense and defense. These values predict next week’s scores, and a bet is placed against the advertised spread. The sum of squares of offensive/defensive point values are constrained to be less than a regularization constant. 79 - Self-organized Deliberative Agent and its Application in Medical Claim Editing Jack Xue, Exec. Application Architect, National Government Services, Anthem, 8115 Knue St., Indianapolis, IN, 46250, United States of America, xinjian.xue@anthem.com In this system each agent self-adjusts its organization per environments before execution and optimizes itself both in structure and in execution steps to meet Service Level Agreement. The scheduling algorithm is formatted as an LP or MILP then generalized to stochastic with uncertainty in phase transitions. The efficacy is demonstrated in a medical claim editing system that identifies irregularities in million claims with calculations of terabyte current and historical data, in near real-time. 80 - Behavioral Analysis of Participants in Community Outreach Intervention Projects Haoxiang Yang, Northwestern University, 2145 Sheridan Road, Room C151, Evanston, IL, 60208, United States of America, haoxiangyang2019@u.northwestern.edu, David Morton, Alexander Gutfraind The Community Outreach Intervention Projects (COIP) serves the Chicago metropolitan area, providing support for drug users to help prevent infectious diseases. Using about 10 years of data, we study the behavior of participants in COIP’s syringe exchange program, focusing on the temporal process governing their visits to storefronts and demographics. With a better understanding of the participants’ behavior, we aim to help develop an improved marketing plan for COIP. 81 - A Schatten-p Norm Perturbation Inequality and its Application in Low Rank Matrix Recovery Man Chung Yue, The Chinese University of Hong Kong, RM 2511,Man Tai House, Tsz Man Est,, Tsz Wan Shan, KLN, Hong Kong, Hong Kong - PRC, mcyue@se.cuhk.edu.hk, Anthony Man-cho So Low-rank matrix recovery, with its applications in finance, network localization, etc, has recently attracted intense research and can be formulated as a rank minimization. Because of the NP-hardness, a common heuristic is to use the Schatten-p norm minimization as a surrogate. However, the equivalence property of this remains elusive and hinges on a conjectured matrix inequality. We prove this conjecture and derive sufficient conditions for low-rank matrix recovery using Schatten-p heuristics. 82 - Demand Prediction and Two-stage Inventory Policy for an Online Flash Sale Retailer Mengzhenyu Zhang, University of Michigan, Stephen M. Ross School of Business, 701 Tappan Ave, Ann Arbor, MI, 48109, United States of America, zhenyuzh@umich.edu We show the work cooperated with an online flash sale retailer in China. With millions history sales records, we use machine learning techniques to predict demand and propose a two stage inventory policy, which requires to response quickly to early-hour real sales and restock inventory thereafter. A model is built to explain positive and negative effects of our policy. Experimenting on real data, we help this firm increase profit by approximately 18% and reduce remaining inventory by over 50%. 83 - Heuristics for Bicycle Sharing System Repositioning Problem Mary Kurz, Clemson University, 110 Freeman Hall, Clemson, SC, 29634, United States of America, mkurz@clemson.edu, Ling Zu This paper studies the static bicycle repositioning problem with real NYC Citi system data. It selects a subset of stations to visit, sequences them, and determines the pick-up/drop off quantities in each visited station. The study incorporates real problem characteristics by minimizing total penalties of lacking/ overflowing bicycles and routing cost. A Variable Neighborhood Search heuristic is introduced to solve the described problem.

84 - Ranking Universities: Practices, Problems and Way Forward Muhammad Mukhtar,Professor, American University of Ras Al Khaimah, (AURAK), Ras Al Khaimah 10021, United Arab Emirates, mukhtar.muhammad@gmail.com, Sarah Mukhtar, Zahida Parveen, Brian Wigdahl We report here a comparison of various global ranking systems of universities and their impacts in the society. Five global ranking systems parameters evalua- tions revealed that Times Higher Education Ranking System is more appropriate when compared with other ranking systems. Our analyses revealed that disci- pline wise ranking by various global rankers are creating dilemmas for the par- ents and public to decide about their children education. We propose normaliza- tion of ranking systems.

Monday, 1:30pm - 3:00pm

MC01 01-Room 301, Marriott Military O.R. and Applications V Sponsor: Military Applications Sponsored Session

Chair: Michael Hirsch, ISEA TEK, 620 N. Wymore Rd., Ste. 260, Maitland, FL, 32751, United States of America, mhirsch@iseatek.com 1 - Predicting the Use of Violence using Machine Learning Methods Erkam Guresen, KHO, Dikmen, Ankara, Turkey, erkamguresen@gmail.com, Salih Tutun, Gulgun Kayakutlu Use of Violence by Ethno–Political Organizations is threatening not only individually countries but also all humanity. As a consequence governments are obliged to take measures in their budget for this threat. Obviously it does not mean that whole of security budgets consist of spending for Use of Violence, however it has important effects on them. For all these reasons, the aim of this study is to examine the predetermine models for use of violence. 2 - Unmanned Aerial Vehicle Routing in the Presence of Threats Kamil Alotaibi, Taibah University, College of Engineering, P.O. Box 344, Almadinah Almunawwarah, PC41411, Saudi Arabia, kamilalotaibi@hotmail.com, Jay Rosenberger, Siriwat Visoldilokpun, Stephen Mattingly We study the routing of Unmanned Aerial Vehicles (UAVs) in the presence of enemy threats. We formulate a mixed integer linear program that maximizes the total number of visited targets for multiple UAVs while maintaining both the route travel time and the total threat level to predetermined constant parameters. Several waypoint generation methods are proposed. Branch and price is used to solve the problem. A computational study is done and results for different scenarios are presented. 3 - Variants of the Target Visitation Problem Michael Hirsch, ISEA TEK, 620 N. Wymore Rd., Ste. 260, Maitland, FL, 32751, United States of America, mhirsch@iseatek.com In this research, we consider the target visitation problem, and discuss some variants. Mathematical formulations are derived, heuristics are developed, and results are presented.

MC02 02-Room 302, Marriott Logistics and Transportation Security Cluster: Homeland Security Invited Session

Chair: Gary Gaukler, Drucker School of Management, Claremont Graduate University, Claremont, CA, 91711, United States of America, Gary.Gaukler@cgu.edu 1 - Cyber Vulnerability Models Murat Karatas, The University of Texas at Austin, 1 University Station Austin TX 78712, United States of America, mkaratas@utexas.edu, Nedialko Dimitrov Infrastructures, such as university nuclear reactors, are controlled through cyber- physical systems. Assessing the vulnerability of these system is key in directing defensive investment. We present an MDP to compute an optimal attack policy. The MDP has an exponential number of states, and is based on tracking the set of available attacks for each link in the network. Surprisingly, we show it is possible to compute values for each MDP state, and optimal attack policies, using s-t reliability.

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