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Shohel Ahmed
School of Engineering and Information Technology, University of New South Wales, Canberra 2612, Australia

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Journal article
Published: 09 August 2018 in Aerospace
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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.

ACS Style

Shohel Ahmed; Sameer Alam; Michael Barlow. A Cooperative Co-Evolutionary Optimisation Model for Best-Fit Aircraft Sequence and Feasible Runway Configuration in a Multi-Runway Airport. Aerospace 2018, 5, 85 .

AMA Style

Shohel Ahmed, Sameer Alam, Michael Barlow. A Cooperative Co-Evolutionary Optimisation Model for Best-Fit Aircraft Sequence and Feasible Runway Configuration in a Multi-Runway Airport. Aerospace. 2018; 5 (3):85.

Chicago/Turabian Style

Shohel Ahmed; Sameer Alam; Michael Barlow. 2018. "A Cooperative Co-Evolutionary Optimisation Model for Best-Fit Aircraft Sequence and Feasible Runway Configuration in a Multi-Runway Airport." Aerospace 5, no. 3: 85.

Conference paper
Published: 09 November 2016 in Proceedings in Adaptation, Learning and Optimization
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In this paper, we present an evolutionary optimization based path planning algorithm at Terminal Airspace (TAS) that provides a near optimal aircraft arrival sequence at Final Approach Fix (FAF). The sequence obtained minimizes the inter-arrival time as well as provides conflict free path planning to an Air Traffic Controller (ATC). A classic Genetic Algorithm (GA) based optimization technique with conflict detection and resolution is developed. Conflict between any two aircraft is detected based on their future arrival time at the waypoint and resolved by stretching the gap between those two aircraft. The proposed algorithm is compared with the traditional GA. Results indicate that the proposed approach obtains a near optimal solution compared to the traditional GA based algorithm which does not consider TAS constraints.

ACS Style

Shohel Ahmed; Sameer Alam; Michael Barlow. An Evolutionary Optimization Approach for Path Planning of Arrival Aircraft for Optimal Sequencing. Proceedings in Adaptation, Learning and Optimization 2016, 1 -16.

AMA Style

Shohel Ahmed, Sameer Alam, Michael Barlow. An Evolutionary Optimization Approach for Path Planning of Arrival Aircraft for Optimal Sequencing. Proceedings in Adaptation, Learning and Optimization. 2016; ():1-16.

Chicago/Turabian Style

Shohel Ahmed; Sameer Alam; Michael Barlow. 2016. "An Evolutionary Optimization Approach for Path Planning of Arrival Aircraft for Optimal Sequencing." Proceedings in Adaptation, Learning and Optimization , no. : 1-16.

Conference paper
Published: 23 January 2016 in Transactions on Petri Nets and Other Models of Concurrency XV
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The airports have emerged as a major bottleneck in the air transportation network. Thus during the busiest time, optimal utilization of the limited airport resources such as runways and taxiways can help to avoid the congestion and delay as well as increase the airport capacity. This problem is further aggravated by use of Hub-Spoke model by airlines which sees a burst of medium size aircraft arrival followed by few heavy aircraft departure. To address this problem, strategic as well as efficient tactical approaches are essential to deal with arrivals and departures. In this paper, we propose an evolutionary optimization approach to maximize the runway throughput capacity for integrated arrival and departure in a single runway scenario. An evolutionary computation based Genetic Algorithm (GA) is developed to optimize and integrate a stream of arriving and departing aircraft sequence for a given time window. The evolved optimal arrival and departure sequencing was analyzed using the Time-Space diagrams for different aircraft configuration.The distribution shows that in Hub airports heavy and large aircrafts are sequenced consecutively where in Spoke airports similar aircraft (i.e., medium (M)-medium (M), large (L)-large (L) and so on) are positioned side by side to reduce the process time. Simulation result also shows that proposed model obtained optimal sequence that takes lower processing time as well as achieves a higher throughput comparing to First Come First Serve (FCFS) approach commonly used for arriving and departing aircraft.

ACS Style

Shohel Ahmed; Sameer alam. An Evolutionary Optimization Approach to Maximize Runway Throughput Capacity for Hub and Spoke Airports. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 313 -323.

AMA Style

Shohel Ahmed, Sameer alam. An Evolutionary Optimization Approach to Maximize Runway Throughput Capacity for Hub and Spoke Airports. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():313-323.

Chicago/Turabian Style

Shohel Ahmed; Sameer alam. 2016. "An Evolutionary Optimization Approach to Maximize Runway Throughput Capacity for Hub and Spoke Airports." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 313-323.