The AI Policy Frontline: Driving Evidence-Based Solutions, gathers leading researchers, policymakers, government officials, and industry experts to address some of the most critical challenges and opportunities at the intersection of Artificial Intelligence and public policy today.
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Inspiring the development of artificial intelligence for the benefit of all
Located in the heart of Quebec’s AI ecosystem, Mila is a community of more than 1,400 researchers specializing in machine learning and dedicated to scientific excellence and innovation.
The AI Policy Frontline: Driving Evidence-Based Solutions, gathers leading researchers, policymakers, government officials, and industry experts to address some of the most critical challenges and opportunities at the intersection of Artificial Intelligence and public policy today.
Founded in 1993 by Professor Yoshua Bengio, Mila today brings together over 140 professors affiliated with Université de Montréal, McGill University, Polytechnique Montréal and HEC Montréal. Mila also welcomes professors from Université Laval, Université de Sherbrooke, École de technologie supérieure (ÉTS) and Concordia University.
High IL1R1 expression predicts poor survival and benefit from stem cell transplant in intermediate-risk acute myeloid leukemia from the Leucegene cohort
Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses t… (see more)actical planning of intermodal railroad operations by introducing a new problem that simultaneously considers three consolidation processes and the management of a heterogeneous railcar fleet. We model the problem with a scheduled service network design with resource management (SSND-RM) formulation, expressed as an integer linear program. While such formulations are challenging to solve at scale, we demonstrate that our problem can be tackled with a general-purpose solver when provided with high-quality warm-start solutions. To this end, we design a construction heuristic inspired by a relax-and-fix procedure. We evaluate the methodology on realistic, large-scale instances from our industrial partner, the Canadian National Railway Company: a North American Class I railroad. The computational experiments show that the proposed approach efficiently solves practically relevant instances, and that solutions to the SSND-RM formulation yield substantially more accurate capacity estimations compared to those obtained from simpler baseline models. Managerial insights from our study highlight that ignoring railcar fleet management or container loading constraints can lead to a severe underestimation of required capacity, which may result in costly operational inefficiencies. Furthermore, our results show that the use of multi-platform railcars improves overall capacity utilization and benefits the network, even if they can locally lead to less efficient loading as measured by terminal-level slot utilization performance indicators.
Socially responsible and beneficial development of AI is a fundamental component of Mila’s mission. As a leader in the field, we wish to contribute to social dialogue and the development of applications that will benefit society.
