scholarly journals Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation

2020 ◽  
Vol 9 (4) ◽  
pp. 100020
Author(s):  
Yassine Yaakoubi ◽  
François Soumis ◽  
Simon Lacoste-Julien
Keyword(s):  
Machine Learning ◽  
Constraint Aggregation ◽  
Airline Crew ◽  
Dynamic Constraint ◽  
Crew Pairing

Dynamic Constraint Aggregation for Solving Very Large-scale Airline Crew Pairing Problems

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Guy Desaulniers ◽  
François Lessard ◽  
Mohammed Saddoune ◽  
François Soumis
Keyword(s):  
Large Scale ◽  
Constraint Aggregation ◽  
Airline Crew ◽  
Dynamic Constraint ◽  
Crew Pairing

Integrated Airline Crew Pairing and Crew Assignment by Dynamic Constraint Aggregation

2012 ◽  
Vol 46 (1) ◽  
pp. 39-55 ◽  
Author(s):  
Mohammed Saddoune ◽  
Guy Desaulniers ◽  
Issmail Elhallaoui ◽  
François Soumis
Keyword(s):  
Constraint Aggregation ◽  
Airline Crew ◽  
Dynamic Constraint ◽  
Crew Pairing

Airline Crew Pairing Optimization

2013 ◽  
pp. 1-22 ◽  
Author(s):  
Laurent Alfandari ◽  
Anass Nagih
Keyword(s):  
Airline Crew ◽  
Crew Pairing

On Large-Scale Airline Crew Pairing Generation

2018 ◽  
Author(s):  
Divyam Aggarwal ◽  
Dhish Kumar Saxena ◽  
Michael Emmerich ◽  
Saaju Paulose
Keyword(s):  
Large Scale ◽  
Airline Crew ◽  
Crew Pairing

scholarly journals Cockpit crew pairing Pareto optimisation in a budget airline

2022 ◽  
Vol 13 (1) ◽  
pp. 67-80 ◽  
Author(s):  
Parames Chutima ◽  
Nicha Krisanaphan
Keyword(s):  
Fuel Cost ◽  
Substantial Cost ◽  
Substantial Cost Saving ◽  
Cost Component ◽  
Airline Crew ◽  
Cost Minimisation ◽  
Crew Pairing ◽  
Differential Algorithm ◽  
Optimal Measurements ◽  
The Cost

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

scholarly journals Column Generation Approaches to a Robust Airline Crew Pairing Model For Managing Extra Flights

2009 ◽  
pp. 439-444
Author(s):  
Elvin Çoban ◽  
İbrahim Muter ◽  
Duygu Taş ◽  
Ş. İlker Birbil ◽  
Kerem Bülbül ◽  
...  
Keyword(s):  
Column Generation ◽  
Airline Crew ◽  
Crew Pairing ◽  
Pairing Model
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