Informs Annual Meeting 2017

MC60

INFORMS Houston – 2017

MC61

2 - State of the Driverless/Smart Driving Technologies Carl D. Van Dyke, TransNetOpt, 6 Snowbird Court, West Windsor, NJ, 08550, United States, carl@cvdzone.com In this round table session, we will examine the state of the driverless/smart driving trucking and railway technologies. We will also discuss how viable it is and how likely it is going to be accepted by the businesses. 3 - Benefits of Driverless Technology when used by Trucking Companies Bruce Patty, Veritec Solutions, San Rafael, CA, United States, bpatty@veritecsolutions.com This paper will provide an overview of the areas where there are benefits expected from a transition to Driverless Technology. Estimates of potential benefits for various recipients will be reviewed. 370A A Spectrum of Business Analytics Curricula Sponsored: INFORMEd Sponsored Session Chair: Susan Wright Palocsay, James Madison University, Harrisonburg, VA, 22807, United States, palocssw@jmu.edu 1 - Implementing an Interdisciplinary Business Analytics and Data Science Program at an Urban University J.D Jayaraman, Associate Professor, New Jersey City University, Jersey City, NJ, United States, jjayaraman@njcu.edu, EunSu Lee One of the challenges in today’s data driven world is educating students in being adept at working with large amounts of data and extracting meaning out of it. This study describes an attempt at meeting this educational challenge with the creation of an interdisciplinary business analytics and data science program at an urban university in New Jersey. Curricular challenges and needs for such a program are detailed. Tools, teaching techniques, industry involvement, marketing and administrative support required for successful implementation are outlined. Challenges and hurdles encountered are also presented. 2 - The Story of Operational Analytics Program at the University of South Dakota Ali Dag, Assistant Professor, University of South Dakota, 414 E. Clark Street, Beacom Hall, Office : 217, Vermillion, SD, 57069, United States, ali.dag@usd.edu, Thomas Tiahrt University of South Dakota’s Beacom School of Business was established in 1927 and has been accredited by AACSB since 1949. The leading business school in the region, Beacom has recently started its new Operational Analytics major at the undergraduate level and Business Analytics focus at the MBA level. In this talk, we will present the structure of the curriculum of these newly created programs and the story behind it. 3 - Experiences with Business Analytics Curriculum Implementation Susan Wright Palocsay, Professor, James Madison University, Msc 0202, Computer Info Sys & Bus Analytics Dept, Harrisonburg, VA, 22807, United States, palocssw@jmu.edu, Ina Samanta Markham, Scott Stevens Advanced analytics is a key technology trend providing a plethora of new opportunities to create business value. Consequently, it has given business schools an opening to introduce extended coverage of statistical analysis, data mining, and OR/MS modeling skills in business education. We will describe our experiences in creating an undergraduate minor in business analytics, including how we have addressed challenges with student capabilities, faculty expertise, recruiters, and limited resources. 4 - Connecting OR/MS to General Studies Michael J.Racer, University of Memphis, 302 Fogelman College of Business, Memphis, TN, 38152, United States, mracer@memphis.edu, Orrin Cooper There’s a huge part of what’s going on in the “real” world that students aren’t grasping or connecting to OR. Interdisciplinary content in OR classrooms is crucial to helping students value OR. Core courses like psychology, political science, and English, suffer from similar stereotypes but can be integrated into OR. One solution is requiring students to bring articles to class. MC60

370B Joint Session MIF/PSOR: Nonprofit Operations and Humanitarian Logistics Sponsored: Minority Issues Sponsored Session Chair: Irem Sengul Orgut, Lenovo, Raleigh, NC, 27612, United States, isengul@ncsu.edu 1 - Supply Chain Management in Limited Data Environments: Application to Community Health Workers Kezban Yagci Sokat, Northwestern University, IEMS, 2145 Sheridan Rd, Evanston, IL, 60208, United States, kezban.yagcisokat@u.northwestern.edu, Karen Smilowitz, Irina Dolinskaya Limited historical data and limited on hand inventory bring up additional difficulty to supply chain management. In collaboration with Last Mile Health, we develop an inventory optimization model for different levels of the supply chain where we link health outcomes to inventory optimization under limited data. 2 - Dynamic Allocation of NGO Funds among Program, Fundraising, and Administration Telesilla Olympia Kotsi, 3400 S Sare Rd, Apt 917, Bloomington, IN, 47401, United States, tkotsi@indiana.edu, Goker Aydin, Alfonso J. Pedraza-Martinez US NGOs report three types of spending: program spending to deliver services directly to beneficiaries; fundraising spending; and administrative spending. Watchdog organizations give higher ratings to NGOs that allocate more of their budget to the program. However, fundraising and administrative spending are also beneficial. Fundraising helps increase the NGO’s future budget, while administrative spending helps make future program spending more impactful. We model this trade-off in a dynamic program. We use financial data from human services NGOs to conduct a case study. Our results show that prioritization of fundraising and administration spending is necessary under certain conditions. 3 - Assessment Routing with Uncertain Travel Times Burcu Balcik, Ozyegin University, Industrial Engineering Department, Nisantepe Mah Orman Sok No 34-36, Cekmekoy, 34794, Turkey, burcu.balcik@ozyegin.edu.tr, Ihsan Yanikoglu This study focuses on rapid needs assessment operations conducted immediately after a disaster occurs. We consider a stochastic selective assessment routing problem, which determines the sites to be visited within a limited amount of time to ensure coverage of different community groups affected by the disaster. We use ambiguous chance constraints to model random travel times and apply a safe approximation technique using robust optimization to obtain a tractable formulation. We present an efficient tabu search algorithm, which obtains high quality solutions for the problem. 4 - Modeling for the Equitable and Effective Distribution of Food Donations under Stochastic Capacities Irem Sengul Orgut, Lenovo, 4944 Summit Arbor Dr, Apt 205, Raleigh, NC, 27612, United States, isengul@ncsu.edu Motivated by our partnership with a local food bank, we present a robust optimization model to support the equitable and effective food distribution over the food bank’s service area. Our model addresses uncertainty in the counties’ capacities which depend on factors such as budget and workforce. Assuming the capacity of each county varies within a range, the model seeks to maximize total food distribution while enforcing a user-specified level of robustness. We derive structural properties of the model and develop an efficient exact solution algorithm. We illustrate our model using historical data, summarize the policy implications of our results and examine the impact of uncertainty on outcomes.

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