A novel column generation strategy for large scale airline crew pairing problems

2016 ◽  
Vol 55 ◽  
pp. 133-144 ◽  
Author(s):  
Bahadır Zeren ◽  
Ibrahim Özkol
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
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 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

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

Solving a robust airline crew pairing problem with column generation

2013 ◽  
Vol 40 (3) ◽  
pp. 815-830 ◽  
Author(s):  
İbrahim Muter ◽  
Ş. İlker Birbil ◽  
Kerem Bülbül ◽  
Güvenç Şahin ◽  
Hüsnü Yenigün ◽  
...  
Keyword(s):  
Column Generation ◽  
Airline Crew ◽  
Crew Pairing ◽  
Pairing Problem

scholarly journals A column generation approach to the airline crew pairing problem to minimize the total person-days

2016 ◽  
Vol 10 (3) ◽  
pp. JAMDSM0040-JAMDSM0040
Author(s):  
Wei WU ◽  
Yannan HU ◽  
Hideki HASHIMOTO ◽  
Tomohito ANDO ◽  
Takashi SHIRAKI ◽  
...  
Keyword(s):  
Column Generation ◽  
Airline Crew ◽  
Crew Pairing ◽  
Pairing Problem

scholarly journals Maximum Variance Hashing via Column Generation

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Lei Luo ◽  
Chao Zhang ◽  
Yongrui Qin ◽  
Chunyuan Zhang
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
Web Search ◽  
Nearest Neighbor ◽  
Computational Cost ◽  
Multimedia Retrieval ◽  
Training Data ◽  
Nonlinear Dimensionality Reduction ◽  
Maximum Variance ◽  
Data Volume

With the explosive growth of the data volume in modern applications such as web search and multimedia retrieval, hashing is becoming increasingly important for efficient nearest neighbor (similar item) search. Recently, a number of data-dependent methods have been developed, reflecting the great potential of learning for hashing. Inspired by the classic nonlinear dimensionality reduction algorithm—maximum variance unfolding, we propose a novel unsupervised hashing method, named maximum variance hashing, in this work. The idea is to maximize the total variance of the hash codes while preserving the local structure of the training data. To solve the derived optimization problem, we propose a column generation algorithm, which directly learns the binary-valued hash functions. We then extend it using anchor graphs to reduce the computational cost. Experiments on large-scale image datasets demonstrate that the proposed method outperforms state-of-the-art hashing methods in many cases.

Airline Crew Pairing Optimization

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