Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty

Cadarso, Luís; Celis, Raúl de

doi:10.1016/j.trc.2017.06.003

Cited by 42 publications

(9 citation statements)

References 38 publications

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“…Wang et al [5] considered a changing passenger arrival rate and proposed an event-driven model to solve the train scheduling problem for an urban rail transit network. Cadarso and de Celis [6] introduced robust itineraries to reduce the number of miss-connected passengers and proposed an integrated model to update base schedules in terms of timetable and fleet assignments while considering stochastic demand and uncertain operating conditions. Wang et al [7] proposed a multiobjective mixed-integer nonlinear programming model to solve the problem of metro train scheduling and rolling stock circulation planning under time-varying passenger demand.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

A Stochastic Programming Approach for Scheduling Extra Metro Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains

Long

Meng

Luan

et al. 2020

Journal of Advanced Transportation

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The metro system is an important component of the urban transportation system due to the large volume of transported passengers. Hub stations connecting metro and high-speed railway (HSR) networks are particularly critical in this system. When HSR trains are delayed due to a disruption on the HSR network, passengers of these trains arriving at the hub station at night may fail to get their last metro connection. The metro operator can thus decide to schedule extra metro trains at night to serve passengers from delayed HSR trains. In this paper, we consider the extra metro train scheduling problem in which the metro operator decides how many extra metro trains to dispatch and their schedules. The problem is complex because (i) the arrival of delayed HSR trains is usually uncertain, and (ii) the operator has to minimize operating costs (i.e., number of additional trains and operation-ending time) but maximize the number of served passengers, which are two conflicting objectives. In other words, the problem we consider is stochastic and biobjective. We formulate this problem as a two-stage stochastic program with recourse and use an epsilon-constrained method to find a set of nondominated solutions. We perform extensive numerical experiments using realistic instances based on the Beijing metro network and two HSR lines connected to this network. We find that our stochastic model outperforms out-of-sample a deterministic model that relies on forecasts of the delay by a range of 3–5%. Moreover, we show that our solutions are nearly optimal by computing a perfect information dual bound and obtaining average optimality gaps below 1%.

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Section: Related Work and Contributionsmentioning

confidence: 99%

A Stochastic Programming Approach for Scheduling Extra Metro Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains

Long

Meng

Luan

et al. 2020

Journal of Advanced Transportation

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“…Kenan et al [8] propose a two-stage planning model based on the uncertainty of demand and fare and solve the model using the method of sample average approximation. Considering the uncertainty of the data, Cadarso and de Celis [9] propose a large-scale mathematical model, which is solved by the Benders decomposition approach. Cui et al [10] proposed 3 models, improved the VNS algorithm, compared the experimental results with commercial solvers, and verifed the efectiveness of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Game‐Theoretic Approach for the Robust Daily Aircraft Routing Problem

Deng

Guo

Li³

et al. 2022

Journal of Mathematics

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In the operation of airlines, the most important link is determining the route and scheduling of aircraft. The key to this problem is to input the flight segment and aircraft type and finally determine all flight segments for each aircraft. In this paper, we focus on finding feasible, robust scheduling for various uncertainties in the flight process. This paper presents a new robust integer mathematical model based on game theory that considers daily aircraft routing. Then, in order to find the suboptimal solution to a large-scale integer programming problem in a limited amount of time, a heuristic algorithm integrating a column generation algorithm and variable domain search is introduced. In addition, we use the data of a Chinese airline to verify, and the experimental results show that the model proposed by us is more robust than the model in general.

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“…Pita et al (2014) proposed the fleet assignment and schedule design model to minimize the overall costs in air transportation network, and they implemented network welfare analysis [22]. Cadarso and Celis (2017) presented an airline planning model integrating schedule design, fleet assignment, and passenger use, considering the stochastic demand value and uncertain operational conditions, to minimize the number of passengers who miss the connecting flight [23]. Faust et al (2017) proposed the schedule design and aircraft maintenance routing model to maximize the profit of medium-sized point-to-point airlines with a homogeneous fleet while considering the demand for the various fare classes [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Takeoff Time Optimization Using Cellular Automaton-Based Simulator

et al. 2021

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Although schedule design has further potential to reduce airline operation costs and flight delay, the effectiveness of the globally optimal schedule design integrating air traffic flow has not been discussed thus far. This paper presents a global multi-objective takeoff time optimization to design efficient flight schedules that lead to minimal congestion and provide sufficient resilience against traffic problems. NSGA-II is adopted as the multi-objective optimization technique in this study. The objective functions include minimization of the total arrival delay and total fuel consumption because these are key performance indicators of air traffic management (ATM). The design variable used in this study is the takeoff time offset of each flight landing at the Tokyo International Airport. 607 design variables were used in this study. The range of the design variables was ±300 s to investigate the effect of a minor variation in the takeoff time. A cellular automaton-based model was utilized to simulate the interaction of the flights with each other. The results of the simulations demonstrated that the obtained optimal solutions could drastically reduce the total arrival delay and total fuel consumption by 1500 min and 80 tons, respectively. The spacing adjustments of one of the optimum flight schedules, in comparison to the original flight schedule, were reduced by 80% in the en-route and terminal airspaces. Additional analyses suggest that it is preferable to have longer takeoff time intervals for flights originating from the same point during congestion hours than those during non-congestion hours. This indicates that the optimization of ground movements in airports improves the efficiency of air traffic operations. INDEX TERMSAir traffic control, air traffic management, cellular automaton, flight scheduling, ground holding program, NSGA-II, schedule design

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Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty

Cited by 42 publications

References 38 publications

A Stochastic Programming Approach for Scheduling Extra Metro Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains

A Stochastic Programming Approach for Scheduling Extra Metro Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains

A Game‐Theoretic Approach for the Robust Daily Aircraft Routing Problem

Multi-Objective Takeoff Time Optimization Using Cellular Automaton-Based Simulator

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