Informs Annual Meeting 2017

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INFORMS Houston – 2017

2 - Intermodal Hub Simulation Mike D.Prince, BNSF Railway, 4853 Grinstein Drive, Fort Worth, TX, 76244, United States, mike.prince@bnsf.com Intermodal hubs are the facilities at which BNSF Railway’s intermodal trains interface with customers. This presentation will discuss an AnyLogic simulation model that was developed for the purpose of assisting in the capital expansion planning process for these facilities. 3 - Investigating General Intermodal Terminal Capacity Relationships with AnyLogic Simulations Darkhan Mussanov, University of Illinois at Urbana-Champaign, Currently, intermodal terminal capacity is largely evaluated on the basis of practitioner planning experience. The industry is in need of a more thorough understanding of the interaction between fundamental factors that influence intermodal terminal capacity. As a first step to create a tool for investigating the factors affecting intermodal terminal capacity, this research develops a simulation model of the intermodal terminal operation. 4 - Managing Intermodal Equipment under Supply and Demand Uncertainties – An Implementation of an Operations Research Tool in a North American Railway Yudi Pranoto, Norfolk Southern Corporation, 1200 Peachtree Street Ne, Box 12-117, Atlanta, GA, 30309, United States, yudi.pranoto@nscorp.com, Clark Cheng Intermodal units such as containers and trailers are transported by train using intermodal flatcars. The demand of intermodal flatcar is varies by location and by day of the week. In this presentation, we will discuss implementation of a forecasting tool to assist intermodal equipment manager to better plan for the availability of intermodal flatcars. Key concepts of prediction interval, network optimization, intermodal yard capacities, and equipment loading rules will also be discussed. RailTEC, 205 N.Mathews Avenue, Urbana, IL, 61801, United States, mussano2@illinois.edu, Tyler Dick 370A Integrating Scholarship with the Teaching of OR/MS/Analytics Sponsored: INFORMEd Sponsored Session Chair: Neil Desnoyers, Saint Joseph’s University, Upper Darby, PA, 19082, United States, ndesnoye@sju.edu 1 - An Experiential Learning Activity for Integrating Optimization and Group Work in the ORMS Classroom Mihai Banciu, PhD, Bucknell University, Lewisburg, PA, United States, mmb018@bucknell.edu Traditionally, topics like linear and integer optimization in a spreadsheet-based modeling course are taught based on a “follow the instructor approach”. We present a web-based interactive activity whereby students can work in small groups in the classroom, while the instructor aggregates the intermediate results into a master problem and provides instant feedback. 2 - Instructional Guidance for Client-Based ORMS Education Susan E. Martonosi, Harvey Mudd College, Department of Mathematics, 301 Platt Boulevard, Claremont, CA, 91711, United States, martonosi@hmc.edu How do we move beyond the theory of ORMS in our classrooms and give students the skills to be practitioners? Many ORMS programs have begun instituting field-based projects serving real clients as part of their curricula. This talk will offer the audience a set of design principles drawn from the myriad approaches described in ORMS educational literature. 3 - An Advance in Web-Based Spreadsheet Modeling Resulting from Classroom Use Neil Desnoyers, Saint Joseph’s University, 133 Green Valley Rd, Upper Darby, PA, 19082, United States, ndesnoye@sju.edu Spreadsheets are moving online. Introduction of an OR/MS case in an MBA classroom helped uncover an issue modeling using Google Sheets and the OpenSolver tool. We apply lessons learned to the feedback loop between teaching and scholarship. 4 - Designing Educational Assessment via Network Optimization Camilo Gómez, Universidad de los Andes, Cra 1 E.No 19 A.- 40, Bogata, Colombia, gomez.ch@uniandes.edu.co, Jorge Huertas, Jorge A. Leal Network models are useful abstractions for a variety of systems. This work explores the application of network flows models in the organizational realm, approaching an education problem in an industrial engineering department, following ABET guidelines and standards. We propose different network TB60

optimization models at different scales of abstraction to promote the interactions between, and within, the courses towards the continuous improvement of the program. While there are challenges in the quantification of non-tangible aspects, the proposed approach proves useful to engage stakeholders in meaningful interactions and convey otherwise abstract features of complex systems.

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370B Sports Analytics II

Sponsored: SpORts Sponsored Session Chair: Stephen Hill, UNC, Wilmington, NC, United States, hills@uncw.edu 1 - Late Game Decision Making in the NFL

Stephen Hill, UNC Wilmington, NC, United States, hills@uncw.edu Late-game decision-making in the National Football League (NFL) has drawn considerable scrutiny in recent years. In this presentation, an analysis of late- game decision-making is presented. In particular, the analysis focuses on field goal versus “going for it” decisions late in close games. 2 - Optimal Defensive Allocation in Baseball Jim Ostrowski, University of Tennessee, 11421 Old Colony Pkwy, Knoxville, TN, 37934, United States, jostrows@utk.edu We will discuss the problem of optimal allocation defensive baseball positions. By integrating open-source collected data into our integer formulation, we are able to of identify an optimal defensive allocation for MLB hitters that can have a significant impact on their batting average. 3 - A Playbook for Teaching Sports Analytics to Undergraduate Business and MBA Students Scott Nestler, University of Notre Dame, 383 Mendoza College of Business, Notre Dame, IN, 46556, United States, snestler@nd.edu The presenter teaches a half-semester length course in Sports Analytics to undergraduates and MBAs at the University of Notre Dame. Offensive plays — reaching out to faculty members at other schools who teach similar courses; allowing students freedom to use familiar tools or coding languages. Defensive plays — selecting Excel as a common language for class examples (may revisit in future with a more pro-style scheme that incorporates R); using a known but somewhat dated text (Winston’s “Mathletics”). Please come listen and share your experiences in teaching quantitative techniques using a subject matter that students are truly excited about. 4 - An Approximate Dynamic Programming Approach to Determine the Optimal Substitution Strategy for Basketball David Hughes, George Mason University, Fairfax, VA, United States, dhughe12@masonlive.gmu.edu Coaches currently use a variety of substitution strategies (such as player rotations and playing the “next best” player). However, such strategies may not be optimal in terms of distributing rest over the course of a whole game. In part, this is because live monitoring of physiological factors is not authorized by the NCAA. Even with such data, it may not be obvious when to rest a skilled player in order to ensure that player is fresh throughout the game. This research develops an approximate dynamic programming (ADP) model of a coach’s lineup decisions accounting for changing players’ endurance levels and the uncertainty of the defensive intensity played against them. 5 - An Interactive Optimization Web App for the NHL Expansion Draft Benjamin Potter, University of Toronto, Mechanical and Industrial Engineering, 5 Kings College Road, Toronto, ON, M5S.3G8, Canada, ben.potter@mail.utoronto.ca, Timothy Chan The Vegas Golden Knights are the newest team to enter the NHL. The league laid out rules for an Expansion Draft where the existing teams protect some of their players from being drafted and Vegas fills its roster by selecting from the remaining players. We developed a web app based on integer programs that model the existing 30 teams’ protection decisions and Vegas’s draft decisions. The app allows fans to optimize the draft using one of a number of performance based metrics. Users can also plan for the future by balancing on-ice performance with financial flexibility objectives.

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