OPTIMA Seminar 20 July 2022 16:00 IN PERSON
Speaker: Hoa Bui, ARC Centre for Transforming Maintenance through Data Science
Title: Optimisation Methods for Maintenance Scheduling in the Mining Industry
Synopsis: Maintenance planning and scheduling are critical in any asset-intensive business, and there is now growing interest in industry on optimisation models that can partially automate the scheduling process and reduce the errors associated with manual scheduling. This talk gives an overview of the presenter’s experience working with the WA mining industry to develop mathematical optimisation techniques for maintenance scheduling. By exploring duality theory in non-smooth optimisation as a bridge between discrete and continuous optimisation, we develop efficient solution methods for mixed-integer quadratic programming models that arise in maintenance scheduling.
If time allows, we will discuss preliminary numerical results for the proposed solution methods for a class of binary non-convex quadratic problems.
Bio: Hoa Bui is an applied mathematician working in variational analysis, non-smooth and non-convex optimisation, combinatorial graph theory and convex analysis. Hoa’s current focus is on developing efficient algorithms for solving large-scale optimisation problems in industry.
Hoa obtained a B.Sc. in Mathematics from the University of Pedagogy, Ho Chi Minh City, Vietnam, in 2016. Soon after graduation, she moved to Australia to pursue a PhD in applied mathematics at Federation University, graduating in 2020.
Hoa is a research fellow at the ARC Centre for Transforming Maintenance through Data Science, which Curtin University hosts. The Centre is funded by Alcoa, BHP, and Roy Hill, and aims to develop the next generation of mathematical and statistical methods for transforming the way maintenance is planned, scheduled, and analysed.
WED 20 JULY 4PM – 5PM AEST MELBOURNE – IN PERSON
Please attend in-person to support our speaker.
Melbourne Connect 700 Swanston St
Level 7, 7212
ZOOM MEETING ID: 873 1557 5255; PASSWORD: 778635