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A 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.
Barry, A.-S., Quesnel, F., El Hallaoui, I., & Soumis, F. (2024). Algorithme primal ajoutant des variables pour le problème du partitionnement d'ensemble généralisé. (Technical Report n° G-2024-11). External link
Quesnel, F., Wu, A., Desaulniers, G., & Soumis, F. (2022). Deep-learning-based partial pricing in a branch-and-price algorithm for personalized crew rostering. Computers and Operations Research, 138, 15 pages. External link
Quesnel, F., Desaulniers, G., & Soumis, F. (2020). A branch-and-price heuristic for the crew pairing problem with language constraints. European Journal of Operational Research, 283(3), 1040-1054. External link
Quesnel, F., Desaulniers, G., & Soumis, F. (2020). Improving Air Crew Rostering by Considering Crew Preferences in the Crew Pairing Problem. Transportation Science, 54(1), 97-114. External link
Quesnel, F. (2019). Trois variantes du problème de rotations pour une approche semi-intégrée de la planification d'horaires de personnel aérien [Ph.D. thesis, Polytechnique Montréal]. Available
Quesnel, F., Desaulniers, G., & Soumis, F. (2017). A new heuristic branching scheme for the crew pairing problem with base constraints. Computers & Operations Research, 80, 159-172. External link
Racette, P., Soumis, F., Quesnel, F., & Lodi, A. (2024). Gaining insight into crew rostering instances through ML-based sequential assignment. Top, 42 pages. Restricted access
Rosat, S., Quesnel, F., El Hallaoui, I., & Soumis, F. (2017). Dynamic penalization of fractional directions in the integral simplex using decomposition: Application to aircrew scheduling. European Journal of Operational Research, 263(3), 1007-1018. External link
Rosat, S., Quesnel, F., Soumis, F., & El Hallaoui, I. (2016, February). Pénaliser les directions fractionnaires dans le simplexe en nombres entiers. Application au transport aérien [Paper]. 17e Congrès de la Société française de recherche opérationnelle et d'aide à la décision (ROADEF 2016), Compiègne, France. Unavailable
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