Comptes rendus du neuvième atelier de résolution de problèmes industriels de Montréal, 19-23 août 2019 / Proceedings of the ninth Montréal industrial problem solving workshop, August 19-23, 2019

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The Ninth Montreal IPSW took place on August 19-23, 2019, and was jointly organized by the CRM and IVADO (Institute for Data Valorization). The workshop welcomed more than 100 participants and allowed nine teams to study problems submitted by the National Research Council of Canada (NRC) and five companies or institutions: Radio-Canada (the French network of the CBC), the Autorité des marchés financiers (AMF) of the Québec Government, Air Canada, Desjardins, and The Co-operators. Four of these companies were already IVADO partners: The Co-operators was not an IVADO partner but made a financial contribution to the workshop, as did the NRC and CANSSI (the Canadian Statistical Sciences Institute).

The problems submitted to the workshop were varied and required expertise from diverse fields. Those submitted by Radio-Canada and the AMF required expertise in NLP (Natural Language Processing): Philippe Langlais (U. de Montréal) and Jian-Yun Nie (U. de Montréal) were the respective coordinators of the teams studying these problems. Air Canada provided the workshop with three problems, i.e., two problems in revenue management and a third on the optimization of a loyalty program (Aeroplan). The three corresponding teams were led (respectively) by Fabian Bastin (U. de Montréal), François Bellavance (HEC Montréal), and Margarida Carvalho (U. de Montréal).

An insurance group within Desjardins submitted a problem on the geographic stratification of risk, whose coordinators were Philippe Gagnon (Oxford and U. de Montréal) and Juliana Schulz (HEC Montréal). The Co-operators submitted a problem on fraud detection: its coordinator was Anas Abdallah (McMaster). The NRC submitted two problems: one on structure representation, whose team was led by Guy Wolf (U. de Montréal), a researcher in artificial intelligence; and another on the unsupervised learning of novel RFI sources. The latter problem was submitted by a group of astronomers and was studied by a team led by Professor Chris Budd (Bath).

, 112 pages

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