The research in this domain include failure process modeling, reliability and remaining useful life estimation, and prognostics and health management (PHM) of various types of assets, subject to time-based or condition-based maintenance. We develop and verify new state-of-the-art methodologies to provide diagnostic and prognostic solutions for mission-critical systems, such as energy systems.
Successful projects
We develop mathematical models for inspection and maintenance optimization of different assets, separately or jointly with inventory spare parts optimization. The models can find the optimal periodic or non-periodic inspection policy for assets subject to hidden failures, or suggest the optimal maintenance actions and time, as well as spare parts inventory order-up-to levels and reorder points.
Successful projects
We develop mathematical models for separately or jointly find the optimal process plans, production schedules, and maintenance policies for manufacturing processes, which result in minimum manufacturing cost, production makespan, as well as energy consumption. We can help energy-intensive industries, such as mining, and iron and steel, reduce significantly their energy consumption and operations costs by planning their process and scheduling their production in a more energy efficient manner.
Successful projects
We develop new scientific methods to help organizations and municipalities make tactical and operational decisions for sustainable asset management. Sustainable asset management is an emerging interdisciplinary research which involves deploying, operations, maintenance, upgrading, and disposal of assets to provide service in a socially, environmentally, and cost-effective manner.
Successful projects
In recent years, the development of emerging technologies, such as smart assets, cloud computing and Internet of Things (IoT) have brought new opportunities and challenges for physical asset management. We provide big data analytics solutions for smart assets replacement/repair/refurbish decisions, proactive maintenance, fleet management, and develop machine learning algorithms for prognostic and health management of assets.
Successful projects
In this research domain, we model mathematically the progression of chronic diseases, such as cancer, and capture the effect of the factors impacting the disease initiation and the progression rates between various stages of the disease development. We also develop mathematical and economic models to study the health benefits and risk of different intervention strategies to detect or treat a disease, such as cancer screening, and find the optimal strategies for sub-populations with different ages and risk factors.
Successful projects
RRMR Lab
Dept. of Mechanical & Industrial Engineering
350 Victoria Street, Toronto, Ontario,
M5B 2K3, Canada
RRMR Lab
338A Eric Palin Hall (EPH)
87 Gerrard Street East, Toronto, Ontario,
M5B 1G6, Canada