Application of genetic algorithm to flight schedule planning

Adachi, Nobue; Sato, Makihiko; Kobayashi, Shinya

doi:10.1002/scj.10036

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Application of GA in scheduling is not a new concept, its applications include: job shop scheduling procedure (Davis, 1985, citied by Goldberg, 1989), schedule optimisation (Syswerda, 1991), concrete sleepers production scheduling (Pérez-Vázquez et al 2004), flight schedule planning (Adachi et al 2004). However, it is noted that the application of GA in maintenance field is almost an untouched area.…”

Section: Applications To the Field Of Built Environmentmentioning

confidence: 99%

Decision Making Approaches for Complex Adaptive Systems in Built Environment

Lee¹

2017

IJMAR

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The decision processes in the construction industry are complex with several dimensions that have to be taken into consideration. These comprise, for instance, of the urban planning, health and safety, facilities management, and energy efficiency. The process of designing and erecting a building usually involves multiple and conflicting objectives such as cost, time, quality, resource utilization, to name a few. The decisions in addressing various aspects concerning the construction process and the sustainability of the end product are numerous and of the conflicting nature. Therefore, finding an appropriate decision making approach is the cornerstone of the effective decision process. This paper proposes the use of biomimicry and genetic algorithms as an alternative to the established practice in decision making for the complex and adaptive system in built environment.

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Section: Applications To the Field Of Built Environmentmentioning

confidence: 99%

Decision Making Approaches for Complex Adaptive Systems in Built Environment

Lee¹

2017

IJMAR

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“…1 the route planning problem (Clarke et al, 1997;Gopalan and Talluri, 1998) 2 fleet assignment problem (FAP) (Adachi et al, 2004;Gu et al, 1994;Hane et al, 1995) because of the difficulties of optimising both issues, and recently integrates them to conduct 3 the route planning and FAP (Barnhart et al, 1998;Gen et al, 2008;. Regular flight (single month)…”

Section: Introductionmentioning

confidence: 99%

“…Gopalan and Talluri (1998) and Clarke et al (1997) Adachi et al (2004, Hane et al (1995) and Gu et al (1994) Gen et al (2008, and Barnhart et al (1998) Regular + non-regular flight (multiple months)…”

Section: Introductionmentioning

confidence: 99%

Evolutionary multi-objective route and fleet assignment optimisation for regular and non-regular flights

Takadama

Jinba²,

Harada

et al. 2016

IJAL

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To optimise the flight schedule that consists of: 1) the regular flight operated on the same day and time through one year; 2) the non-regular flight operated on the different day and time according to month, this paper proposes the new multi-objective fleet assignment method that considers both the regular and non-regular flights. To investigate the effectiveness of our method, this paper applies it to Japanese domestic airport network optimisation for two months, on-and off-peak months, using a real-world data. The intensive simulation have revealed that the following implications: 1) our method can evolve a flight network that can be applied into the on-and off-peak month; 2) our method can find a flight network that has a well-balanced profit between on-and off-peak months; 3) in peak month, our method can find a flight network that has higher profit.

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“…MOGA has been successfully applied on many engineering applications such as [4] and [5]. But until recently, the use of multi-objective approaches for aircraft schedule recovery problems has just started to receive some attention.…”

Section: Introductionmentioning

confidence: 99%

A hybrid multiobjective genetic algorithm on optimizing aircraft schedule recovery problems under short-time response

Chen

Liu

Chou

et al. 2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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This article presents a hybrid multiobjective genetic algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed algorithm extends from the original method of inequality-based multiobjective genetic algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population.Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.

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Application of genetic algorithm to flight schedule planning

Cited by 4 publications

References 4 publications

Decision Making Approaches for Complex Adaptive Systems in Built Environment

Decision Making Approaches for Complex Adaptive Systems in Built Environment

Evolutionary multi-objective route and fleet assignment optimisation for regular and non-regular flights

A hybrid multiobjective genetic algorithm on optimizing aircraft schedule recovery problems under short-time response

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