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Hydrogen airport location selection and fleet assignment under policy considerations

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 197卷

作者： Wu, Jiaxuan Sun, Xiaoqian Xu, Yifan Jiang, Changmin Wandelt, Sebastian Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Beihang Univ State Key Lab CNS ATM Beijing 100191 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hong Kong Peoples R China

The growing environmental awareness has led to increased expectations for lower carbon travel, prompting governments to start implementing policies encouraging airlines to adopt new energy aircraft. This paper examines the effects of various carbon emission reduction policies, such as taxation and stringent carbon emission constraints, on the operational strategy preferences of hydrogen-powered airlines. A fundamental optimization model for hydrogen airport location selection and fleet assignment is developed, along with 16 heterogeneous model variants that integrate different airline operating strategy preferences. Utilizing historical data from Delta Airlines and the carbon emission standards set by ICAO (International Civil Aviation Organization) for 2030 and 2050, the results of our experiments demonstrate significant potential for reducing carbon emissions and increasing airline revenues through differentiated policies.

关键词： Hydrogen-powered aircraft fleet assignment Carbon emissions Airline preference Government policy

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A stochastic hub location and fleet assignment problem for the design of reconfigurable park-and-ride systems

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 184卷

作者： Guillot, Matthieu Rey, David Furno, Angelo El Faouzi, Nour-Eddin Univ Gustave Eiffel ENTPE LICIT ECO7 Champs Sur Marne France Univ Cote dAzur SKEMA Business Sch Sophia Antipolis France

Park -and -ride systems have the potential to improve the efficiency of transportation networks by providing targeted shared mobility services. The design of a park -and -ride system depends on its role in regards to the broader transportation network. Reconfigurable park -and -ride systems aim to provide complementary shared mobility services in the context of varying travel demand scenarios, such as special events, network maintenance operations or non -recurrent perturbations. The design of reconfigurable park -and -ride systems involves the location of access and egress hubs for shared mobility services. We study an extended version of this hub location problem with integrated fleet assignment decisions. We consider stochastic scenarios representative of varying travel demand and traffic conditions on the network and propose a two -stage stochastic integer programming hub location formulation for this problem. First -stage variables represent hub location decision while second -stage variables represent both scenariobased transportation flows and fleet assignment decisions. The latter represent shared mobility service vehicles and they are modeled as integer variables. We develop solution methods to solve this two -stage stochastic integer programming hub location formulation. Exact approaches based on the L-shaped method are proposed with single- and multi -cut configurations. Valid inequalities along with a tight lower bound for the generation of optimality cuts are presented. We also develop a matheuristic to solve larger problem instances. We report numerical results on problem instances based on real data of the city of Lyon, France. We show how stochastic scenarios representative of varying demand and traffic conditions can be generated from such data. Our experiments demonstrate the benefits of this integrated modeling approach for designing efficient reconfigurable park -and -ride systems while considering fleet assignment decisions.

关键词： Two-stage stochastic integer programming L-shaped algorithm Decomposition Matheuristic Hub location fleet assignment Park-and-ride

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Integrated optimisation model for airline bank structure and fleet assignment problem

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ANNALS OF OPERATIONS RESEARCH 2024年第1期342卷 265-285页

作者： Ciftci, Muharrem Enis Ozkir, Vildan Istanbul Atlas Univ Istanbul Turkiye Yildiz Tech Univ Istanbul Turkiye

The airline decision-making process is intended to solve complex operations planning problems sequentially. As computational power increases so do integrated approaches to solving major airline optimisation problems. In general, network carriers encounter more complex integrated problems of fleet planning, aircraft routing and increasing connectivity in the airline network. Bank optimisation problem addresses creating competitive advantage for airline network carriers, by providing better connectivity to passengers via improving their flight schedules in the airline bank structure. The aim of this study is to determine the optimal fleet to cover all flights in a flight schedule which is designed to improve connectivity in the network. The objective function takes account of the overall fleet allocation profitability on the flight schedule, the total worth of passenger connections, route profits, and the penalty cost for using excess slot. We propose a mixed integer linear programming model for the integrated bank optimisation-fleet assignment problem which can be defined as a specific flight scheduling model. To test the model, we have utilised a dataset from a Turkish carrier. The results from the integrated model show that small changes in the bank structure create more efficient schedule and fleet assignment could increase profitability at the same time. The findings imply that network and schedule planners are able to solve two major problems, simultaneously. Advances in the optimisation techniques will increase the application of the integrated models in the other parts of airline planning world, such as operations, crew planning, revenue management.

关键词： Bank optimisation fleet assignment Integrated approach

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fleet assignment Using Population-Based Incremental Learning 60

Fleet Assignment Using Population-Based Incremental Learning

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60th Annual Conference of the Society-of-Instrument-and-Control-Engineers-of-Japan (SICE)

作者： Kobayashi, Yuto Fukuyama, Yoshikazu Meiji Univ Sch Interdisciplinary Math Sci Tokyo Japan

ISBN: (纸本)9784907764739

This paper proposes fleet assignment using population-based incremental learning (PBIL). Effectiveness of the proposed PBIL based method is verified by comparing with the conventional reactive tabu search (RTS) based method and the actual flight operation data. It is verified that the proposed PBIL based method can reduce costs more than the conventional RTS based method and the actual flight operation data.

关键词： fleet assignment population based incremental learning meta-heuristics evolutionary computation

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Integrated flight scheduling and fleet assignment with improved supply-demand interactions

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2021年 149卷 162-180页

作者： Birolini, Sebastian Antunes, Antonio Pais Cattaneo, Mattia Malighetti, Paolo Paleari, Stefano Univ Bergamo Dept Management Informat & Prod Engn Bergamo Italy Univ Coimbra CITTA Dept Civil Engn Coimbra Portugal

Flight scheduling and fleet assignment are important steps of an airline planning process. In light of the reciprocal relationship between air transport supply and demand, a key element of these models is to devise effective methods to both incorporating estimation of total market demand and allocating passengers over the available itineraries in a specific market. In this paper, we present a novel mixed integer nonlinear flight scheduling and fleet assignment optimization model wherein air travel demand generation and allocation are simultaneously and consistently endogenized. Using a nested logit formulation, we jointly model competition among air travel itineraries and appraise the contribution of specific itinerary attributes to demand generation, therefore yielding a more comprehensive and explicit representation of supply-demand interactions. Computational testing based on realistic problem instances reveals that the model can optimize mid-size hub and-spoke networks within reasonable time. Further analyses illustrate the benefits that can be derived from the application of the proposed approach using real-world data for a major European airline. Results demonstrate that the proposed approach can significantly enhance operating profits by up to 6.9% and better reveal opportunities for demand stimulation against a conventional approach using inelastic trip generation. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Flight scheduling fleet assignment Integrated airline planning Demand-supply interactions Piecewise linearization

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Integrated airline fleet introduction and assignment under a daily route-based network

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ANNALS OF OPERATIONS RESEARCH 2024年 1-31页

作者： Jing, Zhou Zhe, Liang Tongji Univ Sch Econ & Management Shanghai Peoples R China

This study investigates an integrated airline fleet introduction and assignment problem under a daily route-based network. It is critical for an airline to integrate advanced digital technology with the methodology of operational research to achieve smart operations. An integer programming model is proposed for this problem. Based on the model, a hybrid algorithm combining a multi-encoding variable neighborhood search (VNS) and Branch-and-Bound (B&B) method is proposed. Firstly, the multi-encoding VNS solves fleet introduction and determines fleet numbers. A self-adaptive multi-layer encoding process with nine neighborhood structures is designed in the VNS process to improve the local search efficiency. Secondly, given the fleet numbers, the improved B&B method driven by profit matrix is designed to solve fleet assignment. There are three modified strategies in the B&B method containing cyclic best-first search, wide branching, and upper bound pruning. Finally, computational experiments are conducted to test algorithm performance. The proposed algorithm is compared with basic VNS, differential evolution, and genetic algorithm on objective value, running speed, and stability. The experiments show that the proposed model and algorithm offer a smart methodology for airline integrated problem.

关键词： fleet introduction fleet assignment Daily route-based network Variable neighborhood search Branch-and-bound

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A mixed-integer nonlinear optimization model for integrated flight scheduling, fleet assignment, and ticket pricing in competitive market

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JOURNAL OF REVENUE AND PRICING MANAGEMENT 2021年第5期20卷 596-607页

作者： Kiarashrad, Masood Pasandideh, Seyed Hamid Reza Mohammadi, Mohammad Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

In the airline industry, ticket pricing is one of the most complex problems that researchers and practitioners are extensively involved on it. In fleet assignment and airline scheduling problems, ticket price is usually taken as an input. This paper presents an integrated model of fleet assignment, airline scheduling, and ticket pricing in a network of competitive market. Four essential factors are considered as important factors that can affect ticket pricing. The model is formulated as a mixed-integer nonlinear programming, and as it is NP-Hard, a tabu search metaheuristic method is proposed to solve the model. Results show that airport dominance, route dominance, time dominance, and flag carrier dominance have influence on average ticket price in the market and model finds the best time and routes for flights to increase pricing power and eventually airline profit.

关键词： Flight scheduling fleet assignment Ticket pricing Tabu search

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Choice-Based Airline Schedule Design and fleet assignment: A Decomposition Approach

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TRANSPORTATION SCIENCE 2022年第6期56卷 1410页

作者： Yan, Chiwei Barnhart, Cynthia Vaze, Vikrant Univ Washington Dept Ind & Syst Engn Seattle WA 98195 USA MIT Operat Res Ctr 77 Massachusetts Ave Cambridge MA 02139 USA MIT Sloan Sch Management 77 Massachusetts Ave Cambridge MA 02139 USA Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA

We study an integrated airline schedule design and fleet assignment model for constructing schedules by simultaneously selecting from a pool of optional flights and assigning fleet types to these scheduled flights. This is a crucial tactical decision that greatly influences airline profits. As passenger demand is often substitutable among available fare products (defined as a combination of an itinerary and a fare class) between the same origin-destination pair, we present an optimization approach that includes a passenger choice model for fare product selections. To tackle the formidable computational challenge of solving this large-scale network design problem, we propose a decomposition approach based on partitioning the flight network into smaller subnetworks by exploiting weak dependencies in network structure. The decomposition relies on a series of approximation analyses and a novel fare split problem to allocate optimally the fares of products that are shared by flights in different subnetworks. We present several reformulations that represent fleet assignment and schedule decisions and formally characterize their relative strengths. This gives rise to a new reformulation that is able to trade off strength and size flexibly. We conduct detailed computational experiments using two realistically sized airline instances to demonstrate the effectiveness of our approach. Under a simulated passenger booking environment with both perfect and imperfect forecasts, we show that the fleeting and scheduling decisions informed by our approach deliver significant and robust profit improvement over all benchmark implementations and previous models in the literature.

关键词： airline network design fleet assignment schedule design decomposition passenger choice

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Integrated fleet assignment and scheduling for environmentally

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期138卷 103567-103567页

作者： Justin, Cedric Y. Payan, Alexia P. Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

Regional air mobility refers to the transportation of passengers aboard small aircraft over short distances. These operations are challenging due to low passenger volumes, competing transportation modes, and high operating costs. Nevertheless, the recent convergence of new technologies for electric propulsion and autonomy brings the promise of step-changes in both operating efficiency and sustainability. The next challenge is to define concepts of operations that best use these advanced aircraft. We develop a modeling and optimization environment to address this challenge by first estimating the passenger demand for regional air mobility, and then by concurrently optimizing the fleet assignment and scheduling of these operations. This helps identify the optimum fleet composition and the network of routes that best serve this demand. The contribution of this research is the formulation of the optimization as a half leg half-itinerary mixed-integer linear program using a hierarchical multi-objective approach that provides insights about trade-offs between profitability and emissions. We demonstrate the ability of this environment to solve large fleet assignment and scheduling problems to near optimality by applying it to the United States Northeast Corridor using a fleet of electric and hybrid-electric regional aircraft. Results highlight that the transition to an electrified fleet allows serving twice the number of communities currently served, while reducing the carbon emissions per passenger by fifty percent. With improving battery specific energy density, many more untapped markets can be served profitably and sustainably, and many smaller communities can get connected to the rest of the National Airspace System.

关键词： Scheduling fleet assignment Demand modeling Sustainability Regional air mobility Mixed-integer linear programming Electric aircraft

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Airline Timetable Development and fleet assignment Incorporating Passenger Choice

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TRANSPORTATION SCIENCE 2020年第1期54卷 139-163页

作者： Wei, Keji Vaze, Vikrant Jacquillat, Alexandre Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA MIT Sloan Sch Management Cambridge MA 02142 USA

Flight timetabling can greatly impact an airline's operating profit, yet data-driven or model-based solutions to support it remain limited. Timetabling optimization is significantly complicated by two factors. First, it exhibits strong interdependencies with subsequent fleet assignment decisions of the airlines. Second, flights' departure and arrival times are important determinants of passenger connection opportunities, of the attractiveness of each (nonstop or connecting) itinerary, and, in turn, of passengers' booking decisions. Because of these complicating factors, most existing approaches rely on incremental timetabling. This paper introduces an original integrated optimization approach to comprehensive flight timetabling and fleet assignment under endogenous passenger choice. Passenger choice is captured by a discrete-choice generalized attraction model. The resulting optimization model is formulated as a mixed-integer linear program. This paper also proposes an original multiphase solution approach, which effectively combines several heuristics, to optimize the network-wide timetable of a major airline within a realistic computational budget. Using case study data from Alaska Airlines, computational results suggest that the combination of this paper's model formulation and solution approaches can result in significant profit improvements as compared with the most advanced incremental approaches to flight timetabling. Additional computational experiments based on several extensions also demonstrate the benefits of this modeling and computational framework to support various types of strategic airline decision making in the context of frequency planning, revenue management, and postmerger integration.

关键词： timetable development fleet assignment passenger choice mixed-integer programming

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