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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Pereira, P., Courtade, E., Aloise, D., Quesnel, F., Soumis, F., & Yaakoubi, Y. (2022). Learning to branch for the crew pairing problem. (Technical Report n° G-2022-31). External link
Tahir, A., Quesnel, F., Desaulniers, G., El Hallaoui, I., & Yaakoubi, Y. (2021). An Improved Integral Column Generation Algorithm Using Machine Learning for Aircrew Pairing. Transportation Science, 55(6), 1411-1429. External link
Yaakoubi, Y., Soumis, F., & Lacoste-Julien, S. (2021, July). Structured Convolutional Kernel Networks for Airline Crew Scheduling [Paper]. International Conference on Machine Learning (ICML 2021). External link
Tahir, A., Quesnel, F., Desaulniers, G., El Hallaoui, I., & Yaakoubi, Y. (2020). An improved integral column generation algorithm using machine learning for aircrew pairing. (Technical Report n° G-2020-71). External link
Yaakoubi, Y., Soumis, F., & Lacoste-Julien, S. (2020). Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation. EURO Journal on Transportation and Logistics, 9(4), 14 pages. Available
Yaakoubi, Y., Soumis, F., & Lacoste-Julien, S. (2020). Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation. (Technical Report n° G-2020-13). External link
Yaakoubi, Y. (2019). Combiner intelligence artificielle et programmation mathématique pour la planification des horaires des équipages en transport aérien [Ph.D. thesis, Polytechnique Montréal]. Available
Yaakoubi, Y., Lacoste-Julien, S., & Soumis, F. (2019). Flight-connection prediction for airline crew scheduling to construct initial clusters for OR optimizer. (Technical Report n° G-2019-26). External link
Soumis, F., Yaakoubi, Y., & Lacoste-Julien, S. (2019, June). Machine Learning → Mathematical Programming for Air Crew Scheduling [Paper]. 10th Triennial Symposium on Transportation Analysis conference (TRISTAN X), Hamilton Island, Australia (5 pages). External link
