2015 Informs Annual Meeting

TB05

INFORMS Philadelphia – 2015

3 - Protecting and Restoring Facilities from Intentional Attacks Chi Zhang, Assistant Professor, Tsinghua University, 100084 Beijing, Department of Industrial Engineering, Beijiing, China, czhang@tsinghua.edu.cn, Sachuer Bao Besides protecting facilities from intentional attacks, another paramount issue is taken into consideration- restoring destroyed facilities to optimal service level within a given time interval. The defender decides which facilities to protect before an attack, and resource allocation between improving capacities of operational facilities and rebuilding destroyed facilities after an attack, to maximize profit by satisfying customer demands. The problem is solved by an ant colony algorithm. 4 - Coordinating Pre- and Post-disaster Resource Allocation at Multiple Locations Fei He, Assistant Professor, Texas A&M University-Kingsville, 700 University Blvd., Kingsville, TX, 78363, United States of America, fei.he@tamuk.edu, Jun Zhuang Resource allocation in the face of disaster aims to improve the efficiency and effectiveness of disaster relief. In this research, disaster preparedness and relief at multiple locations are modeled in a two-stage stochastic programming framework with the objective of loss minimization. New insights of coordinating preparedness and relief at multiple locations are provided. 5 - Model Validation for a Public-private Partnerships Model in Disaster Management Vineet Madasseri Payyappall, PhD Student, University at Buffalo, 305 Winspear Avenue (Upper), Buffalo, NY, 14215, United States of America, vineetma@buffalo.edu, Peiqiu Guan, Jun Zhuang This research designed and conducted an experiment to validate a public-private partnerships model in disaster management. A two-staged experiment was conducted in a computer simulated environment. The risk behaviors of the subjects were evaluated in the first stage, and the second stage collected the decision of the subjects, who played the role of the private sectors, under different scenarios. The experiment shows that our model results are consistent with the experimental results. 1 - Coordinating Subcontractor Scheduling with Divisible Jobs Behzad Hezarkhani, Assistant Professor, Nottingham University Business School, Jubilee Campus, Nottingham, United Kingdom, behzad.hezarkhani@nottingham.ac.uk, Wieslaw Kubiak We study a decentralized scheduling problem with a single subcontractor and several agents having divisible jobs. Under complete information, we design pricing schemes that always make the agents’ decisions coincide with efficient schedules. Under private information, we prove that the pivotal mechanism makes truth-telling the only optimal choice of the agents when announcing their processing times. We comment on the subcontractor’s revenue under complete and private information. 2 - Single Machine Scheduling via Decision Theory J.J. Kanet, Department of MIS/OM/DSC, 300 College Park, We consider the following procedure for scheduling a single machine. At time t the machine is free with a set N of n jobs ready to occupy it. Thus, we have n choices for jobs to occupy the machine starting at time t with the remaining n-1 jobs completed later. Given that a job k is tentatively chosen to next occupy the machine, we calculate its completion time and the expected value (E) of the completion times of the remaining n-1 jobs. We do this for each of the n choices, producing for each the set of completion times C = {Cj?j?N}. We then evaluate the objective Z = f(C) choosing that job k?Z is minimum to next occupy the machine. We provide an unbiased estimator of the set C and show that the procedure provides optimum results when the objective Z is to minimize flow time or maximum tardiness. 3 - Scheduling on a SIngle Machine under Time of Use Tariffs Kan Fang, Tianjin University, No 92 Weijin Road, Nankai District, Tianjin, 300072, China, zjumath@gmail.com, Nelson Uhan We consider the problem of scheduling jobs on a single machine to minimize the total electricity cost of processing these jobs under time-of-use electricity tariffs. We show the computational complexity of this problem for both the uniform- speed and speed-scaling cases, present different approximation algorithms for the speed-scaling case and analyze their computational performance. We also show how to compute optimal schedules for the preemptive version of the problem in polynomial time. University of Dayton, Dayton, OH, 45419-2130, United States of America, Kanet@udayaton.edu TB03 03-Room 303, Marriott New Topics in Scheduling Cluster: Scheduling and Project Management Invited Session Chair: Rainer Kolisch, rainer.kolisch@wi.tum.de

4 - The Value of Flexibility and Shift Extensions in Physician Scheduling

Andreas Fögener, University of Augsburg, Universitätsstrafle 16, Universität Augsburg, WiWi, Augsburg, De, 86159, Germany, andreas.fuegener@unikat.uni-augsburg.de, Jens Brunner Scheduling physicians is a relevant topic in hospitals. In the literature, demand is usually assumed to be deterministic. However, surgery durations and emergencies contain uncertainty. We model stochastic physician demand using a scenario- based approach. We introduce flexible shift extensions, where physicians might have to work longer to match supply with demand and simultaneously increase predictability of working hours. We propose a mixed-integer model and a column generation heuristic to solve our problem. TB04 04-Room 304, Marriott The Business of Music and Emotion in Social Media Cluster: Social Media Analytics Invited Session Chair: Chris Smith, TRAC-MTRY, 28 Lupin Lane, Carmel Valley, 93924, United States of America, cmsmith1@nps.edu 1 - Philippine Language and Emotion During Typhoon Haiyan/Yolanda Amanda Andrei, Graduate Student, Georgetown University, aa1436@georgetown.edu An investigation of language and emotion in tweets from the Philippines before and after 2013 supertyphoon Haiyan/Yolanda using Linguistic Inquiry and Word Count (LIWC), breakpoint analysis, and a computational clustering tool revealed differences in topics and emotions depending on whether messages were expressed in English or Filipino. 2 - Subscribe or Sell: Itunes vs. Google Play Music all Access Hooman Hidaji, PhD Student, University of Alberta, #1604 8515 112 St. NW, Edmonton, Al, T6G1K7, Canada, hooman.hidaji@ualberta.ca Recently, subscription has become a popular method of user monetization in online media business along with selling model. It is expected that firms utilize both approaches to cover as much demand as possible. However, pricing strategy of the firms is crucial in determining the demand for the two. In this study, using an economic model with endogenous demand, we set to model how the firm decides on the business model. Different user types and business model- dependent demand are considered. 3 - Stock Market Prediction using Disparate Data Sources Bin Weng, Auburn University, 425 Opelika Rd Apt. 224, Auburn, Al, 36830, United States of America, bzw0018@auburn.edu, Fadel Megahed Stock market prediction has attracted much attention from academia as well as business. In recent years, social media is considered as a new source to affect human’s behavior and decision-making. In this paper, we will develop a new way to predict the movement of the stock market using disparate data source, social media data and market data. In order to predict the stock price more accurately, the model is developed using multivariable selection method and machine learning statistic methods. Social Media in Business Cluster: Social Media Analytics Invited Session Chair: Dokyun Lee, Carnegie Mellon University, Pittsburgh, PA, United States of America, leedokyun@gmail.com 1 - Understanding the Impact of Discussions on Quality of Crowdsourced Content – The Case of Wikipedia Srikar Velichety, PhD Student, Eller College of Management, University of Arizona, 1130 E Helen St, Tucson, AZ, 85719, United States of America, srikarv@email.arizona.edu, Jesse Bockstedt, Sudha Ram We investigate the impact of discussions on the quality of Crowdsourced content using a data science approach that involves conducting an exploratory study to uncover the associations among different discussion characteristics and article quality and building a prediction model. By identifying appropriate instruments to overcome selection, we build a model to quantify the impact of these characteristics. Our results show that most of these characteristics have a positive impact on quality. TB05 05-Room 305, Marriott

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