Conférences - Recherche opérationnelle - Canada

Innovative tools for reliability analysis and decision making in design, operation and maintenance of engineering systems in today’s connected era, are critical for safe, reliable and effective operation of these systems. The 9th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modelling (APARM 2020) to be held at the Pan Pacific Hotel, Vancouver, Canada (August 21 to 24, 2020) aims to provide a friendly platform for synergistic exchanges of ideas and practices among academics and industry practitioners. The conference will cover a broad range of research and application topics relevant to reliability and maintenance.

Queueing theory is concerned with developing and investigating mathematical models of systems where “customers” wait for “service.” The terms “customers” and “servers” are generic. Customers could, for example, be humans waiting in a physical line or waiting on hold on the telephone, jobs waiting to be processed in a factory, or tasks waiting for processing in a computer or communication system. The CanQueue annual conference started from a workshop, organized by Prof. Alfa, at the University of Manitoba in 1999. Since then, twenty (20) CanQueue conferences have been held in Canada, including CanQueue 2002 at U of S; 2004 at UBC Okanagan, 2006 and 2011 at Banff, both sponsored by BIRS. It was a decision at the CanQueue2019, at Fields Institute in Toronto in August 2019 that CanQueue 2020 will be organized by us and held at Banff (based on the feedback from Canadian queueing community people).

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Uncertainty pervades all areas of the natural sciences, engineering, and operations research. Such uncertainty may arise due to noisy measurements, model ambiguity, or unknown parameters. Moreover, optimal decision making under uncertainty is crucial to arrive at reliable solutions, which are resilient to catastrophe. Despite the inherent need to do so, including uncertainty in our models in the form of random parameters leads to a veritable explosion in the dimension of the resulting optimization or control problems; especially for models of complex engineering systems. Recent advances in the fields of uncertainty quantification and machine learning present exciting opportunities to help combat this curse of dimensionality. This workshop is intended is to join researchers in optimization under uncertainty, uncertainty quantification, and machine learning, whose work stands to benefit from cutting-edge machine learning techniques for intricate, data-driven models of real-world phenomena.

Cours et évènements pour étudiants en mathématiques,    Agents logiciels et intelligence artificielle