Informs Annual Meeting 2017

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INFORMS Houston – 2017

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is derived, a heuristic is developed, and numerous Monte-Carlo simulations are performed to assess solution quality.

351C Air Traffic Flow Management Sponsored: Aviation Applications Sponsored Session Chair: Guglielmo Lulli, g.lulli@lancaster.ac.uk 1 - Strong IP Models for Ground Delay Program Planning

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351E Supply Chain Management Contributed Session Chair: Lorena Pradenas, Universidad de Concepcion, Concepcion, Chile, lpradena@udec.cl 1 - The Impact of Product Variety on Subassembly Inventory and Strategic Assembly Sequencing Jeonghan Ko, Ajou University, Suwon-si, Korea, Republic of, jeonghan@umich.edu Jeonghan Ko, University of Michigan, Ann Arbor, MI, 48105, United States, jeonghan@umich.edu, Heng Kuang This paper identifies the impact of product variety on the safety inventory of subassemblies in serial assembly processes when limited component commonality exists among products. We derive a measure to relate the levels of the product variety and safety inventory. We show that the measure can be used to optimally sequence assembly processes to reduce subassembly inventory levels. We extend the results as a prioritized sequencing principle for more generalized cases. 2 - Field Study of Supply Chain Management Practices of Small-and- medium Enterprise (SME) Retailers Shardul Phadnis, Malaysia Institute for Supply Chain Innovation, 2A, Persiaran Tebar Layar, Seksyen U8, Shah Alam, 40150, Malaysia, sphadnis@misi.edu.my This study describes the nature of supply chain management practices of small- and-medium enterprise (SME) retailers in an emerging market, based on a multi-method field study. Our findings suggest that constrained supply of key resources, specifically operating cash and management skills, influences the retailers’ strategies for procuring the merchandise, warehousing and transporting the goods, and operating the retail stores. We contrast the observed practices with the best practices advocated in the literature, assess the suitability of both sets of practices to the operating environment, and provide normative guidelines for Gang Wang, Assistant Professor, University of Massachusetts Dartmouth, 285 Old Westport Rd, Room 214, North Dartmouth, MA, 02747, United States, gwang1@umassd.edu This paper considers a big-data driven, integrated supply chain design, with the focus on efficient supply chain design analytics. The supply chain is three-echelon consisting of contracted suppliers, capacitated processing centers, and demand points. It first presents a methodology for identifying potential locations for the processing centers using sales data in ERP and GIS data. Then a mixed-integer linear programming model is to determine the optimal number of processing centers, along with efficient algorithm and computational testing. 3 - Recruitment Stocking Problems Anh Tuan Ninh, William & Mary, 1604 Queens Crossing, Williamsburg, VA, 23185, United States, ninhtuananh@gmail.com We explore a general class of inventory control problem - the recruitment stocking problem, which can be found in clinical trials, marketing research/new product launch, as well as inventory management for end-of-life-cycle products. Both exact and approximation methods to measure key performance metrics for the system will be presented. 6 - Prediction of Consumption of Materials with Artificial Neural Networks Lorena Pradenas, Universidad de Concepcion, Casilla 160-C Correo 3, Concepcion, 4030000, Chile, lpradena@udec.cl, Camila Flores This study focuses on the use neural networks to effectively predict the consumption of multiple materials and spare parts of a forestry company and compare the results obtained with those achieved through traditional methods. Today, company analysts must manually assign the forecasting model that best suits the behaviors of each of the more than 100,000 materials coded.. In addition to this, there are hardware limitations, which do not allow the analysis of information in a consolidated way to make global decisions. Clustering methods and unconventional forecasting models were used to obtain predictions with better performance. improving the performance of the supply chain practices. 4 - Big Data for Integrated Supply Chain Design

Alexander Estes, University of Maryland-College Park, College Park, MD, United States, aestes@math.umd.edu, Michael O. Ball The Federal Aviation Administration uses ground delay programs to manage congestion at an airport when the capacity of that airport to accept incoming flights has been impacted by some external factor. We present a multi-stage integer programming model for planning ground delay programs. This model has a strong LP relaxation, and we can show that every constraint of the formulation is facet-defining for the convex hull of integer programs. We also present computational results. 2 - A Passenger-centric Approach to Air Traffic Flow Management Alexandre Jacquillat, Carnegie Mellon University, Heinz College, 5000 Forbes Avenue, Pittsburgh, PA, 15213, United States, ajacquil@andrew.cmu.edu Existing Air Traffic Flow Management (ATFM) approaches are based on aircraft- centric objectives. However, the ultimate impact of flight delays and cancellations on aviation stakeholders are amplified by passenger misconnections on one-stop or two-stop itineraries. We present a novel model of network-wide ATFM operations that considers simultaneously flight delay costs and passenger disruption costs. Results suggest that passenger disruptions can be greatly reduced through no, or limited, increases in flight delays, thus making the outcomes of ATFM initiatives more consistent with airline and passenger preferences. Implications for implementation are discussed. 3 - Secondary Landing Slot Allocation Mechanisms for Ground Delay Programs When congestion is expected at an airport, the Federal Aviation Administration issues a Ground Delay Program (GDP) to allocate landing slots in accordance with the predicted arrival capacity. Once these slots are initially allocated, there is a Collaborative Decision Making step in which airlines can swap slots among their own flights, and also decide cancellations. There has been limited literature on secondary slot allocation mechanisms for the inter-airline trading of slots. We analyze the effectiveness of various secondary slot allocation methods with respect to cost savings, fairness, and incentive compatibility to different airlines. 4 - Mining Alternative Flight Trajectories from Data Repositories: An Application to the European Airspace Luigi De Giovanni, University of Padua, 63 via Trieste, Dipartimento di Matemtica “Tullio Levi Civita”, Padova, I-35121, Italy, luigi@math.unipd.it, Giovanni Andreatta, Lorenzo Capanna, Luca Righi Optimization models for Air Traffic Flow Management aim at determining flight trajectories that reduce congestion of both airports and en-route sectors, and maximize the efficiency of the Air Traffic Management system. To select suitable trajectories, models should consider many factors, such as length, time, en-route charges, fuel consumption, aircraft specifications, airspace users’ preferences etc.,that are not always fully known. We present a data driven approach to mine alternative 2D, 3D or 4D trajectories from the historical flight records stored in the Eurocontrol DDR2 data sources, in order to develop and feed mathematical models for the optimization of trajectory based operations. 351D Analytics and Homeland Security Sponsored: Military Applications Sponsored Session Chair: Andrew Oscar Hall, United States Military Academy, United States Military Academy, West Point, NY, 10996, United States, andrew.hall@usma.edu 1 - Shortest Paths for Routing Information Over Temporally Dynamic Communication Networks Azar Sadeghnejad, University at Buffalo, Buffalo, NY, United States, azarsade@buffalo.edu, Michael Hirsch, Hector Ortiz-Pena This research will examine the effect degraded or limited communication networks have on routing collected time-critical information during a mission. This has applications in military as well as civil situations. A mathematical model Jackie W. Baek, MIT, 77 Massachusetts Ave, Bldg E40-103, Cambridge, MA, 02139, United States, baek@mit.edu, Hamsa Balakrishnan MB34

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