An optimal algorithm for a two runway scheduling problem

The aircraft scheduling problem consists in sequencing aircraft on airport runways and in scheduling their times of operations taking into consideration several operational constraints. It is known to be an NP-hard problem, an ongoing challenge for both researchers and air traffic controllers.The aim of this paper is to present a focused review on the most relevant techniques in the recent literature (since 2010) on the aircraft runway scheduling problem, including exact approaches such as mixed-integer programming and dynamic programming, metaheuristics, and novel approaches based on reinforcement learning. Since the benchmark instances used in the literature are easily solved by high-performance computers and current versions of solvers, we propose a new data set with challenging realistic problems constructed from real-world air traffic.

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“…Multi-objective dynamic programming is also proposed in the literature to solve the RSP [34,35,36,37].…”

Section: Dynamic Programmingmentioning

confidence: 99%

The aircraft runway scheduling problem: A survey

Ikli

Mancel

Mongeau

et al. 2021

Computers & Operations Research

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“…If the objective is to reduce the total delay of the aircraft, similar dynamic programming algorithms can be developed [10]; however, the state space required for such dynamic programming algorithms becomes quite large, and this leads to implementations that are slow for real-time computation. This is particularly true for multiple runway problems, as computationally shown in [8]. Another approach to addressing this computational issue is to reformulate the RSP as a problem with multiple objectives, while retaining the same state space as done by Psaraftis [9].…”

Section: Introductionmentioning

confidence: 97%

“…In [7], the authors solve a simpler variant of the RSP with only departures where there are chaintype precedence constraints for the aircraft. This approach was also extended to two runways in [8].…”

Section: Introductionmentioning

confidence: 98%

“…First, considering multiple objectives provides a way to compare all the feasible schedules across different objectives and find a subset of feasible schedules that provides a good tradeoff. Second, it is known [7], [8] that the variants of the single-objective RSP, where the goal is to minimize the total delay of the aircraft, can be transformed into an equivalent multiobjective RSP and faster 3 algorithms can 1 The makespan of a sequence of aircraft using a runway is defined as the time when the last aircraft in the sequence starts to use the runway. 2 Runway throughput is defined as the number of aircraft using the runway per hour.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Departure Runway Scheduling Using Dynamic Programming

Montoya

Rathinam

Wood

2014

IEEE Trans. Intell. Transport. Syst.

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At busy airports, air traffic controllers seek to find schedules for aircraft at the runway that aim to minimize delays of the aircraft while maximizing runway throughput. In reality, finding optimal schedules by a human controller is hard to accomplish since the number of feasible schedules available for the scheduling problem is quite large. In this paper, we pose this problem as a multiobjective optimization problem, with respect to total aircraft delay and runway throughput. Using principles of multiobjective dynamic programming, we develop an algorithm to find a set of Pareto-optimal solutions that completely specify the nondominated frontier. In addition to finding these solutions, this paper provides a proof of the algorithm's correctness and gives an analysis of its performance against a baseline algorithm using the operational data for a model of the Dallas/Fort Worth International Airport.Index Terms-Aircraft scheduling, multiple objective dynamic programming, Pareto-optimality, runway scheduling.

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“…This approach considers the trade-off between airport arrivals and departures, as well as the coordination among the hub airports. A generalized dynamic programming is proposed for optimal scheduling of two runways [29]. In this approach, the same type of aircraft is grouped together as a batch, and precedence constraints are applied to sequence the aircraft.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Co-Evolutionary Optimisation Model for Best-Fit Aircraft Sequence and Feasible Runway Configuration in a Multi-Runway Airport

2018

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A careful arrival and departure sequencing of aircraft can reduce the inter-arrival/departure time, thereby opening up opportunities for new landing and/or take-off slots, which may increase the runway throughput. This sequence when serviced with a suitable runway configuration may result in an optimal aircraft sequence with a runway configuration that can process the maximum number of aircraft within a given time interval. In this paper, we propose a Cooperative Co-evolutionary Genetic Algorithm (CCoGA) to find the combined solution of a best-fit sequence with a feasible runway configuration for a given traffic demand at an airport. The aircraft sequence and the runway configuration are modelled as individual species, which can cooperatively interact with each other. Therefore, we computationally evolve the best possible combination of aircraft sequence (arrival and departure) and the feasible runway configuration. The proposed CCoGA algorithm is evaluated for Chicago O’Hare International Airport runway layout and resulting configurations. Arrival and departure traffic demand is modelled through a Poisson distribution. Two different arrival/departure sequencing methods, i.e., constraint position shifting with one, two and N-position shifting and first come first serve, are modelled. Runway configuration and traffic sequence (arrivals and departure) are modelled as two species, which are evolved co-operatively, through the CCoGA algorithm, to achieve the optimal traffic sequencing with a feasible runway configuration. Time-space diagrams are presented for the best-evolved population of arrival-departure sequence and runway configuration to illustrate the possibility of using available departure slots between arrivals to maximize capacity. Arrival-departure capacity envelopes are then presented to illustrate the trade-off between the arrivals and departures, given a runway configuration for each sequencing method. Results demonstrate the high mutual dependence between arrival-departure sequence and the runway configuration, as well as its effect on overall runway capacity. The results also demonstrate the viability of using evolutionary computation-based methods for modelling and evaluating complex problems in the air transport domain.

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An optimal algorithm for a two runway scheduling problem

Cited by 10 publications

References 35 publications

The aircraft runway scheduling problem: A survey

The aircraft runway scheduling problem: A survey

Multiobjective Departure Runway Scheduling Using Dynamic Programming

A Cooperative Co-Evolutionary Optimisation Model for Best-Fit Aircraft Sequence and Feasible Runway Configuration in a Multi-Runway Airport

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