The Toronto Intelligent Decision Engineering Laboratory


Postdoctoral Fellowship Opportunity in AI Planning and Mathematical Programming

   TIDEL Research Laboratory

TIDEL Research Group

News feed

6/2016
Accepted paper to ECAI 'Mathematical Programming Models for Optimizing Partial-Order Plan Flexibility'

6/2016
Accepted paper to CP 'Constraint Programming for Strictly Convex Integer Quadratically-Constrained Problems'

6/2016
Accepted paper to CP 'A Constraint Programming Approach to Multi-Robot Task Allocation and Scheduling in Retirement Homes'


The Toronto Intelligent Decision Engineering Laboratory (TIDEL)

is concerned with the structure, organization and manipulation of information for automated and human decision making.

Our primary research focus is the extension of AI and optimization techniques to this end. As a result, we are interested in methodologies such as constraint programming, local search, hybrid AI/OR techniques, stochastic optimization, machine learning, data mining, and representation and reasoning about preferences.

The applications we are currently studying include: scheduling, supply chain management, algorithm control, decision-making in uncertain and dynamic environments, and development of intelligent restaurant and hotel reservation systems. Other broad areas of interest include security, planning and scheduling of web services, automated composition and software engineering.


   TIDEL Research Group

TIDEL is composed of graduate students working toward master's and doctorate degrees in industrial engineering and computer science, interns and post-docs. Members of TIDEL have diverse educational backgrounds, with undergraduate and graduate degrees from computer science, engineering science, industrial engineering, mechatronics and operations research, obtained from Canadian as well as international universities. This diversity is perfect for TIDEL's interdisciplinary approach to research: the variety of perspectives allows the team to gain a deeper understanding of problems and promotes the development of hybrid methodologies for solving them.


University of Toronto Mechanical and Information Engineering