Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

MB33

3 - Data-driven Dynamic Smart Locker Logistics Shahab Derhami, Georgia Tech-ISYE, 755 Ferst Drive, NW, Atlanta, GA, 30332, United States, Satya Sarvani Malladi, Louis Faugere, Benoit Montreuil, Chelsea C. White We consider the problem of dynamic management of mobile locker storage on a multi-location network. We model the non-stationary uncertainty of demand for lockers in our problem and propose effective heuristic solution approaches.

n MB35 North Bldg 224A Joint Session AAS/Practice Curated: AAS Best Student Presentation Competition II Sponsored: Aviation Applications Sponsored Session Chair: Susan Hotle, Virginia Polytechnic Institute and State University, 1 - A Game-Theoretic Analysis of the Scaled Airline Preferences Mechanism for Airport Landing Slots Jackie W. Baek, Massachusetts Institute of Technology, 77 Massachusetts Ave, Bldg E40-103, Cambridge, MA, 02139, United States As arrival capacities increasingly constrain the air transportation system, there is a need for mechanisms by which airlines can exchange landing slots. Currently, when the number of aircraft is projected to exceed the capacity, flights are allocated slots in a first-scheduled-first-served manner. However, flights have different delay costs and can be assigned more efficiently. We focus on a reallocation mechanism called scaled airline preferences (SAP) and evaluate it on individual rationality, incentive compatibility, and fairness. The flight delay cost functions are scaled where the average unit delay cost by airline is equal and the mechanism minimizes the total scaled delay cost. 2 - Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning Xiaojia Guo, University College London, International Hall, Lansdowne Terrace, London, WC1N 1AS, United Kingdom Air passengers missing their connection can have a major impact on satisfaction and airline delays. Accurate forecasts of the flow of passengers and their journey times through an airport can help improve the experience of connecting passengers and support airline, airport, and air space punctuality. In collaboration with Heathrow Airport, we utilize real-time data to develop a predictive system based on a regression tree and Copula-based simulations. These real-time predictions can be used to inform target off-block time adjustments and determine resourcing levels at security and immigration. 3 - An Assessment of the Potential Benefits of Dynamic Airline Scheduling Ahmet Esat Hizir, Massachusetts Institute of Technology, Cambridge, MA, United States, The commonly used approach to airline schedule design does not enable airlines to effectively adapt to changes in passenger demand and airspace system capacity. This study investigates the potential benefits of a dynamic scheduling approach in which flight frequencies, schedules and aircraft types are finalized closer to the day of operations based on the most current demand information. Our integrated schedule design and fleet assignment model satisfies the passenger demand without inconveniencing passengers, to evaluate the maximum possible benefits of a dynamic scheduling strategy. n MB36 North Bldg 224B Resource Allocation in Capacity-constrained Airport Networks Sponsored: Aviation Applications Sponsored Session Chair: Konstantinos G. Zografos, Lancaster University, \LA1 4YX, United Kingdom Co-Chair: Alexandre Jacquillat, Carnegie Mellon University, Pittsburgh, PA, 15213, United States 1 - A Passenger-centric Approach to Air Traffic Flow Management Alexandre Jacquillat, Carnegie Mellon University, Heinz College, 5000 Forbes Avenue, Pittsburgh, PA, 15213, United States Existing Air Traffic Flow Management (ATFM) approaches are based on aircraft- centric objectives. However, the ultimate impact of delays is amplified by passenger misconnections on connecting itineraries. We present a novel approach to flow management that balances flight delay costs with passenger delays. We develop a dual approach involving an analytical Markov Decision Process model and a large-scale integer optimization model. 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 practice are discussed.

n MB33 North Bldg 222C

Joint Session ORAM/CYBER: CyberManufacturing Systems: Emerging Challenges and Opportunities Emerging Topic: OR and Advanced Manufacturing Emerging Topic Session Chair: Mohammed Shafae, Virginia Tech, Blacksburg, VA, 24061, United States 1 - Challenges and Opportunities in Additive Manufacturing for Industry 4.0 Bianca Maria Colosimo, Politecnico di Milano, Via La Masa, 1, Milan, I-20156, Italy This contribution discusses opportunities and challenges for quality assessment, monitoring and control of Additive Manufacturing (AM) processes and products. Special attention is devoted to in-situ data gathering and modeling. 2 - Advancing the Security of Cybermanufacturing Systems: Challenges and Opportunities Lee Wells, Western Michigan University, 2827 Daventry Ave., Portage, MI, 49024, United States, Mohammed Shafae As technology progresses, cyber-physical systems are becoming susceptible to a wider range of attacks. In manufacturing, these attacks pose a significant threat to ensuring products conform to their original design intent and to maintaining the safety of equipment, employees, and consumers. This talk discusses the importance of research and development of cyber-security tools specifically designed for manufacturing. A critical review of current research efforts will be presented as well as opportunities for the future of this emerging research area. 3 - Blockchain in Manufactuirng - A Critical Review Soundar Kumara, Pennsylvania State University, PA, United States, Qianyu Hu Blockchain is becoming popular in several domains. In this talk we explore the technical foundations of Blockchain and their applicability in (big-M ) manufacturing. We will discuss provenance, platforms and the relevance of blockchain. With specific example in manufacturing, we will explore the integrated use of blockchain-analytics and AI. We will address the questions on hyperledger and Ethereum platforms. n MB34 North Bldg 223 11:00 - 11:45 SAS/11:45 - 12:30 Gurobi Vendor Demo Session 1 - Building and Solving Optimization Models with SAS Edward P. Hughes, SAS Institute, Inc., Sas Institute Inc., Sas Campus Drive, Cary, NC, 27513, United States, Rob Pratt SAS provides a broad and deep array of data and analytic capabilities, including data integration, statistics, data and text mining, econometrics and forecasting, and operations research. The SAS optimization, simulation, and scheduling features coordinate easily and fully with other SAS strengths in data handling, analytics, and reporting. OPTMODEL from SAS provides a powerful and intuitive algebraic optimization modeling language and unified support for building and solving LP, MILP, QP, NLP, CLP, and network-oriented models. And because the OPTMODEL optimization modeling language is contained within the OPTMODEL procedure, a SAS software module, it integrates seamlessly with the entire family of SAS functions, procedures, and macros. We’ll demonstrate how you can use OPTMODEL to solve both basic and advanced problems, highlighting its newer capabilities and its support for both standard and customized solution strategies. 2 - Advanced Heuristics with Gurobi Daniel Espinoza, Senior Developer, Gurobi Optimization, Houston, TX, United States This talk covers one capability of MIP that is often overlooked: its ability to find and subsequently improve good quality solutions to exceedingly difficult problems. In particular, we will focus on techniques for using the Gurobi MIP solver as a heuristic, and a discussion on what makes a model more amenable to optimization.

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