2015 Informs Annual Meeting

MB05

INFORMS Philadelphia – 2015

4 - Command and Control Metrics in Studies of Unit Structure and Effectiveness Doug Samuelson, InfoLogix, Inc., 8711 Chippendale Court, Annandale, VA, 22003, United States of America, samuelsondoug@yahoo.com The recent Marine Corps Combat Development Command study, “Composition of the Infantry Battalion,” raised a number of issues, especially: better metrics to assess likely leader effectiveness; leadership structures, communication methods and protocols; decision-making about logistics and intelligence; and the extent to which joint training improves coordinated effect. We summarize findings to date and sources cited and suggest future assessments of unit structure and resulting effectiveness.

3 - Makespan Minimization on Parallel Machine Subject to Machine Release Times and Minimum Total Completion Time Yumei Huo, Associate Professor, City University of New York,

College of Staten Island, 2800 Victory Blvd. 1N-215, Staten Island, NY, 10314, United States of America, Yumei.Huo@csi.cuny.edu

We study the preemptive bi-criteria scheduling problem on m parallel machines such that machines have different release times and once the machines are released, they are always available. The goal is to minimize the makespan subject to the constraint that the total completion time is minimized. We show that there is an optimal polynomial time algorithm for this problem. 4 - Latest Developments in Supply Chain Scheduling Zhi-Long Chen, Professor, University of Maryland, Robert H. Supply chain scheduling studies detailed order scheduling issues in the supply chain and addresses a variety of applications in the real world. Supply chain scheduling is a relatively new area with about 15 years of history. A large body of literature on supply chain scheduling problems has appeared in academic journals including some surveys. Research interest in this area is still growing. We report the latest developments in this area. MB04 04-Room 304, Marriott Panel: Topics for PhD students Sponsor: Minority Issues Forum Sponsored Session Chair: Maria Mayorga, Associate Professor, University of North Carolina, Dept. of Industrial & Systems Engineering, Campus Box 7906, Raleigh, NC, 27695-7906, United States of America, memayorg@ncsu.edu 1 - Topics of Interest for PhD Students Moderator: Maria Mayorga, Associate Professor, University of North Carolina, Dept. of Industrial & Systems Engineering, This session will serve as a panel discussion on topics of interest for PhD students nearing graduation. Topics include: - deciding on industry versus academia - how to prioritize objectives towards then end of the PhD Process - work/life balance when pursuing tenure - networking to achieve a desired faculty position - how to position yourself when pursuing the market - networking at conferences such as INFORMS Smith School of Business, College Park, MD, 20742, United States of America, zchen@rhsmith.umd.edu Campus Box 7906, Raleigh, NC, 27695-7906, United States of America, memayorg@ncsu.edu MB05 05-Room 305, Marriott Tutorial: Analyzing Social Media with LIWC Cluster: Social Media Analytics Invited Session Chair: Sara Beth Elson, Behavioral Scientist, MITRE Corporation, 7515 Colshire Drive, McLean, VA, United States of America, Selson@mitre.org 1 - Tutorial: Analyzing Social Media with LIWC Sara Beth Elson, Behavioral Scientist, MITRE Corporation, 7515 Colshire Drive, McLean, VA, United States of America, Selson@mitre.org This tutorial will introduce the Linguistic Inquiry and Word Count (LIWC) software – a tool that can enable users to track emotion levels expressed in social media across time. Attendees will walk through an example of how to analyze social media using LIWC and how to view the emotion levels expressed.

MB02 02-Room 302, Marriott MAS Tutorial: A Brief Introduction To Predictive Analytics Sponsor: Military Applications Sponsored Session

Chair: Greg Parlier, Past President, MAS of INFORMS, 255 Avian Lane, Madison, AL, 35758, United States of America, gparlier@knology.net 1 - A Brief Introduction to Predictive Analytics Thomas Willemain, Smart Software, Inc., Niskayuna, NY, United States of America, TomW@smartcorp.com This tutorial will introduce a few key methodologies in the field of predictive analytics: extrapolative time series forecasting, linear and logistic regression, and tree models including random forests. The emphasis will be on matching methods to problems, understanding the inputs required by and outputs supplied by the methods, and perspectives on the strengths and weaknesses of the methods.

MB03 03-Room 303, Marriott Supply Chain Scheduling Cluster: Scheduling and Project Management Invited Session

Chair: Zhi-Long Chen, Professor, University of Maryland, Robert H. Smith School of Business, College Park, MD, 20742, United States of America, zchen@rhsmith.umd.edu 1 - Integrated Production and Delivery with Multiple Factories and Customers Joseph Leung, New Jersey Institute of Technology, 4202 GITC,Department of Computer Science, Newark, United States of America, joseph.y.leung@njit.edu, Xun Zhang, Ba-Yi Cheng, Kai Li We consider a scheduling problem where machines are geographically distributed and hence the production costs are different. The delivery costs are also different, depending on where the products are produced. Given a threshold U of the total cost, we want to minimize the makespan or total completion time, subject to the constraint that the total cost is not more than U. Heuristics are proposed and their performances are evaluated through computational studies. 2 - Personnel Scheduling and Supplies Provisioning in Emergency Relief Operations Lian Qi, Rutgers Business School, Department of Supply Chain

Management &, Rutgers, United States of America, lianqi@business.rutgers.edu, Lei Lei, Michael Pinedo, Shengbin Wang, Jian Yang

The practice of emergency operations often involves travelling of medical teams and distribution of medical supplies. The coordination of the scheduling of the medical teams and supplies is critical. We introduce a math programming based rolling horizon heuristic that is able to quickly find near optimal solutions. A polynomial time solvable case, which leads to the design of the proposed heuristic, is discussed. Managerial insights drawn from numerical studies are provided.

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