Time-Indexed Formulations for the Runway Scheduling Problem

Avella, Pasquale; Boccia, Maurizio; Mannino, Carlo; Vasilyev, Igor

doi:10.1287/trsc.2017.0750

Cited by 35 publications

(23 citation statements)

References 26 publications

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“…One popular approach is heuristic or meta-heuristic designed to meet the stringent computation time feature required by the real-time nature. Another is exact-based perspective, which consists of two classic types: dynamic programming (DP) and mixed-integer programming (MIP) [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic Runway Scheduling Problem With Partial Distribution Information of Random Parameters

et al. 2020

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Air traffic flow management is one of the most important operations in terminal airports heavily relying on advanced intelligence transportation techniques. This work considers a two-stage runway scheduling problem given a set of flights with uncertain arrival times. The first-stage problem is to identify a sequence of aircraft weight classes (e.g., Heavy, Large and Small) that minimizes runway occupying time (i.e., makespan). Then the second-stage decision is dedicated to scheduling the flights as punctually as possible after their arrival times realized, which translates into determining a sequence of flights for each aircraft category such that the total deviation time imposed on the flights is minimized. Instead of an exactly known probability distribution, information on uncertain parameters is limited (i.e., ambiguous), such as means, mean absolute deviations and support set of random parameters derived from historical data. Under this information on the random parameters, an ambiguous mixed-integer stochastic optimization model is proposed. For such a problem, we approximately construct a worst-case discrete probability distribution with three possible realizations per random parameter, and adopt a hybrid sample average approximation algorithm in which genetic algorithms are used to replace commercial solvers. To illustrate the effectiveness and efficiency of the proposed model and algorithm, extensive numerical experiments are carried out.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic Runway Scheduling Problem With Partial Distribution Information of Random Parameters

et al. 2020

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“…Shop problems with few jobs occur when modeling synchronization and conflicts among processes share common resources. Examples of this situation include scheduling robot moves in flexible robotic cells (Agnetis et al [6]), aircraft scheduling during taxiing at an airport so that no aircraft collides (Avella et al [7]), or, in container terminals, the synchronization of crane gantry movements once transportation tasks have been assigned (Briskorn and Angeloudis [8]).…”

Section: Introductionmentioning

confidence: 99%

Some Results on Shop Scheduling with S-Precedence Constraints among Job Tasks

2019

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We address some special cases of job shop and flow shop scheduling problems with s-precedence constraints. Unlike the classical setting, in which precedence constraints among the tasks of a job are finish–start, here the task of a job cannot start before the task preceding it has started. We give polynomial exact algorithms for the following problems: a two-machine job shop with two jobs when recirculation is allowed (i.e., jobs can visit the same machine many times), a two-machine flow shop, and an m-machine flow shop with two jobs. We also point out some special cases whose complexity status is open.

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“…Many exact and heuristic algorithms have been proposed to maximize runway capacity by optimising the aircraft scheduling, sequencing and landing [26,30,31]. A single runway-based integrated arrival/departure management approach has been proposed by [32]. This approach utilizes the compact time-indexed formulation defined through clique inequality that is solved by mixed-integer programming.…”

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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Time-Indexed Formulations for the Runway Scheduling Problem

Cited by 35 publications

References 26 publications

Stochastic Runway Scheduling Problem With Partial Distribution Information of Random Parameters

Stochastic Runway Scheduling Problem With Partial Distribution Information of Random Parameters

Some Results on Shop Scheduling with S-Precedence Constraints among Job Tasks

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

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