Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

MC36

additional schedule disturbances, as well as passenger and baggage misconnections. Our preliminary experiments indicate that our metaheuristic approach shows promising results in terms of both solution quality and time. 3 - Strategic Behaviors in Airport Capacity Allocation Mechanisms Weilong Wang, Carnegie Mellon University, Pittsburgh, PA, United States We develop an original bi-level game-theoretic approach to identify opportunities for strategic behaviors by non-atomistic users (e.g. airlines) in non-monetary mechanisms for infrastructure (e.g. airport) capacity allocation. We show that gaming opportunities are limited under a primary mechanism, where capacity is allocated to individual flights. In contrast, airlines may have strong gaming opportunities in a secondary mechanism, where capacity is allocated to airlines who may then swap their own flights. We present computational results comparing the overall performance of both mechanisms. 4 - Investigating Autonomous Air Operations Centers for On-Demand Mobility Networks Victoria Nneji, Duke University, Durham, NC, United States On-Demand Mobility (ODM) in aviation has gained popularity in recent years, with several manufacturers proposing vehicles for air taxis. However, less attention has been placed on how the networked fleets would be managed. Through the development of concepts of operations for remote management of vehicles, this research presents preliminary requirements for ODM centers. This effort identified key requirements related to vehicle safety for these futuristic concepts. Further, this work introduces a model of human-system performance in these centers. With this tool, people making strategic decisions can prototype concepts of operations to better plan for staffing and design of the centers. Chair: Keji Wei, Dartmouth College, Hanover, New Hampshire 1 - Effects of Enterprise Bargaining and Agreement Clauses on Operating Cost of Airline Ground Crew Scheduling Cheng-Lung Wu, UNSW Sydney, School of Aviation, Kensington, NSW 2052, Australia,, Shao Xuan Lim Collective enterprise bargaining empowers airlines to negotiate labour conditions but it is not always clear how labour conditions affect costs. In this study, integer programming models are used to generate check-in staff rosters by considering individual roster conditions. Results showed that the shorter the shift length, the lower the rostering cost, saving around 30% by reducing shift length from 8 to 4 hours. With a ratio of 37% full-time staff in the workforce, there is a further 4.5% reduction in rostering cost. Cost saving can be redirected to employees as wage rises and improve job security. 2 - Airline Timetable Development and Fleet Assignment Incorporating Passenger Choice Keji Wei, Dartmouth College, Thayer School of Engineering at We introduce an original integrated optimization approach to comprehensive timetabling and fleet assignment under endogenous passenger choice. An original multi-phase solution approach and several acceleration heuristics are proposed. Our solution approach significantly outperforms direct implementation using a commercial solver. Computational results using a major airline’s network suggest that our overall modeling and computational approach results in significant profit improvements within a realistic computational budget. We present several extensions for strategic decision-making. 3 - An Assessment of the Potential Benefits of Dynamic Airline Scheduling 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. Dartmouth, 14 Engineering Dr,, Hanover, NH, 03755, United States, Vikrant Vaze, Alexandre Jacquillat Ahmet Esat Hizir, PhD Student, Massachusetts Institute of Technology, Cambridge, MA, United States, Vikrant Vaze, Cynthia Barnhart n MC36 North Bldg 224B Airline Schedule Design Sponsored: Aviation Applications Sponsored Session

n MC34 North Bldg 223 1:30 - 2:15 Lindo Systems, Inc/2:15- 3:00 FICO Vendor Demo Session 1 - Optimization Modeling Tools from LINDO Systems Mark A. Wiley, LINDO Systems Inc, 1415 No Dayton Street, Chicago, IL, 60622, United States, Gautler Laude Exceptional ease of use, wide range of capabilities, and flexibility have made LINDO software the tool of choice for thousands of Operations Research professionals across nearly every industry for over 30 years. LINDO offers solvers to cover all your optimization needs. The Linear Programming solvers handle million variable/constraint problems fast and reliably. The Quadratic/SOCP/Barrier solver efficiently handles quadratically constrained problems. The Integer solver works fast and reliably with LP, QP and NLP models. The Global NLP solver finds the guaranteed global optimum of nonconvex models. The Stochastic Programming solver has a full range of capabilities for planning under uncertainty. Get the tools you need to get up and running quickly. LINDO provides a set of intuitive interfaces to suit your modeling preference. What’s Best! is an add-in to Excel that you can use to quickly build models that managers can use and understand. LINDO has a full featured modeling language for expressing complex models clearly and concisely, and it has links to Excel and databases that make data handling easy. LINDO API is a callable library that allows you to seamlessly embed the solvers into your own applications. Pick the best tool for the job based upon who will build the application, who will use it, and where the data reside. Technical support at LINDO is responsive and thorough - whether you have questions about the software or need some modeling advise. Get started today. Visit our booth or www.lindo.com to get more information and pick up full capacity evaluation licenses. 2 - End-to-End FICO Register Xpress Insight Tutorial: From Data to Decisions for Non-Technical Business Users James T. Williams, FICO, 2665 Long Lake Rd, Building C, Roseville, MN, 55113, United States You have a team with a great analytics background. They have developed advanced analytical tools using Python, R, or with your current traditional optimization solver. They have derived crucial insights from your data, and they’ve figured out how your decisions shape your customers’ behaviors. Now it’s time to put these critical analytical insights in the hands of your non-technical business users. In this tutorial, we will cover how FICO’s Optimization Suite (including Xpress Mosel, Xpress Workbench, and Xpress Insight) makes it possible to embed your analytic models in business user-friendly applications. Learn how you can supercharge your analytic models with simulation, optimization, reporting, what-if analysis, and agile extensibility for your ever-changing business. n MC35 North Bldg 224A AAS Best Student Presentation Competition III Sponsored: Aviation Applications Sponsored Session Chair: Susan Hotle, Virginia Polytechnic Institute and State University 1 - An Approximate Dynamic Programming Approach for Aircraft Maintenance Scheduling Optimization Qichen Deng, Delft University of Technology, Kluyverweg 1, Delft, 2629 HS, Netherlands We propose an approximate dynamic programming approach to optimize the maintenance check for a fleet of heterogeneous aircraft. It is the first time to optimize long-term aircraft maintenance schedule considering different check types (A-/C-/D-checks), merging different check types (A-check into C-check and D-check into C-check), maintenance intervals of different aircraft usage parameters (flight hours, cycles and calendar days) and uncertainty of maintenance elapsed time. The goal is to maximize aircraft availability, reduce maintenance costs, and minimize the unused flight hours of fleet in 3-5 year planning horizon and the decision variables are the start dates of each check. 2 - A Variable Neighborhood Search Approach for the Flight-to-Gate Reassignment Problem Moschoula Pternea, University of Maryland, College Park, MD, 20740, United States The reallocation of flights to gates in case of schedule disruptions is a key airport recovery operation. Especially in hub airports, disruptions affect connecting passengers by causing failed connections or lost baggage. At the same time, introducing passenger transfers in mathematical programming models makes the problem intractable. In this context, we develop a Variable Neighborhood Search approach to generate near-optimal solutions with the objective to minimize

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