2019 SSHRC Grants awarded
Congratulations to the Desautels professors who received 2019 SSHRC Grants.
SSHRC Insight Development Grants
MMA Students Gain Hands-on Experience at Pratt & Whitney Canada
As core to the Master of Management in Analytics (MMA) program, the EXP Analytics Consulting module has MMA students working alongside industry professionals over a 10-month period solving a significant Data & Analytics problem, aimed to boost the client鈥檚 top or bottom lines.
Take a look at what our MMA students have to say after working with partner Pratt & Whitney Canada.
Professor Juan Camilo Serpa awarded 2019 SSHRC Insight Development Grant
Juan Camilo Serpa, Associate Professor in Operations Management, awarded 2019 SSHRC Insight Development Grant
McGill meets rising demand for specialty business degrees
Masters degrees in fields like finance and big data are quickly becoming a popular alternative to the traditional MBA for students looking to specialize. Professor Mehmet Gumus, Academic Director for the Master of Management in Analytics (MMA), and聽聽Rida Mehdi (MMA'19) weigh in on the unique advantages of a specialty degree.
Mehmet Gumus appointed to Editorial Board of Manufacturing & Service Operations Management
Mehmet Gumus, Associate Professor in Operations Management, was recently appointed to the Editorial Board of Manufacturing & Service Operations Management (M&SOM).
Professor Mehmet Gumus awarded 2019 NSERC Discovery Grant
Mehmet Gumus, Associate Professor in Operations Management was recently awarded a 2019 NSERC Discovery Grant.
MMA students find inspiration on inaugural Study Trip in San Francisco
The McGill Master of Management in Analytics (MMA) degree is a pre-experience, twelve-month specialized program in the evolving field of management analytics with a strong emphasis on experiential learning.
As part of this hands-on learning, the first cohort of MMA students went on the program's inaugural Study Trip to San Francisco in June.
Wei Qi awarded 2019 NSERC Discovery Grant
Congratulations to Wei Qi,聽Assistant Professor in Operations Management, awarded 2019 NSERC Discovery Grant聽鈥淭owards a Smart-City Future: Urban-Scale Integration of Mobility and Energy Systems鈥�.
Wei Qi awarded 2019 FRQSC New Academics Grant
Congratulations to聽Wei Qi,聽Assistant Professor in Operations Management, awarded the 2019 FRQSC New Academics Grant (Soutien 脿 la recherche pour la rel猫ve professorale)聽鈥淟e partage de la mobilit茅 durable dans les villes intelligentes鈥� (鈥淪haring Sustainable Mobility in Smart Cities鈥�).聽
Celebrating excellence in teaching at Desautels
Across programs and subject areas, the Desautels Faculty of Management recognizes the vital role that teaching plays in enriching the student experience and in inspiring the next generation of leaders.
On May 16, the Desautels community gathered to recognize faculty who have gone above and beyond in their teaching. Congratulations to the following recipients of the 2018-19 teaching awards!
Building a better business through data analytics
Discover how Professor Juan Serpa and a team of Desautels students are using data analytics to streamline operations for Santropol Roulant.
Read more in the McGill Reporter
Igniting the power of data analytics
Discover how Professor Juan Serpa and the 16 Desautels students comprising a new food analytics club are revolutionizing the operations of beloved NPO Santropol Roulant 鈥� and it鈥檚 all through the power of data analytics.
Read more in the McGill Reporter
Low-complexity method for hybrid MPC with local optimality guarantees
Authors: Damian Frick, Angelos Georghiou, Juan L. Jerez, Alexander Domahidi, and Manfred Morari
Publication:聽SIAM Journal on Control and Optimization, Forthcoming
础产蝉迟谤补肠迟:听
A Primal-Dual Lifting Scheme for Two-Stage Robust Optimization
Authors: Angelos Georghiou, Angelos Tsoukalas, Wolfram Wiesemann
Publication:聽Operations Research, Forthcoming
础产蝉迟谤补肠迟:听
Two-stage robust optimization problems, in which decisions are taken both in anticipation of and in response to the observation of an unknown parameter vector from within an uncertainty set, are notoriously challenging. In this paper, we develop convergent hierarchies of primal (conservative) and dual (progressive) bounds for these problems that trade off the competing goals of tractability and optimality: While the coarsest bounds recover a tractable but suboptimal affine decision rule approximation of the two-stage robust optimization problem, the refined bounds lift extreme points of the uncertainty set until an exact but intractable extreme point reformulation of the problem is obtained. Based on these bounds, we propose a primal-dual lifting scheme for the solution of two-stage robust optimization problems that accommodates for generic polyhedral uncertainty sets, infeasible problem instances as well as the absence of a relatively complete recourse. The incumbent solutions in each step of our algorithm afford rigorous error bounds, and they can be interpreted as piecewise affine decision rules. We illustrate the performance of our algorithm on illustrative examples and on an inventory management problem.
Performance guarantees for model-based Approximate Dynamic Programming in continuous spaces
Authors: Paul N. Beuchat, Angelos Georghiou, and John Lygeros
Publication:聽IEEE Transactions on Automatic Control, Forthcoming
础产蝉迟谤补肠迟:听
