Maker Movement as Part of Industry 4.0

Oliveira, Maquele Antunes De

doi:10.14488/ijcieom2020_full_0010_37434

Cited by 1 publication

(2 citation statements)

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“…Over the past few decades, an increasing effort has been made to recover the flight schedule after disruption both in practice and in the literature (Arıkan et al , 2017). In airline recovery problem, cumulative surveys and review papers were presented by Kohl et al (2007), Clausen et al (2010), Fernando et al (2020), Hassan et al (2021) and Su et al (2021). Kohl et al (2007) reviewed the impact of disruption management and large-scale disruptions on airline performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It included both the traditional and modern approaches of airline recovery problem and their different networks. Fernando et al (2020), Hassan et al (2021) and also Su et al (2021) represented further researches as a systematic review of optimization models in aircraft assignment and airline disruption management. Below, the related researches are reviewed based on aircraft, crew and passenger recovery as well as multistage and integrated approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

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A mathematical model for integrated aircraft and crew recovery after a disruption: a Benders’ decomposition approach

Khiabani

Komijan

Ghezavati

et al. 2022

JM2

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Purpose Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost. Design/methodology/approach A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model. Findings The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited. Originality/value The authors confirm that this is an original paper and has not been published or under consideration in any other journal.

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