The Toronto Intelligent Decision Engineering Laboratory

Toronto Intelligent Decision Engineering Laboratory (TIDEL)

TIDEL Research Group

News Feed

02/2019
Papers accepted to ICAPS2019: 'Learning Scheduling Models from Event Data' and 'Relaxed BDDs: An Admissible Heuristic for Delete-Free Planning Based on a Discrete Relaxation'.
02/2019
Paper accepted to CPAIOR2019: 'A Constraint Programming Approach to Electric Vehicle Routing with Time Windows'.
11/2018
Article accepted to INFORMS Journal on Computing: 'An MDD-based Lagrangian Approach to the Multi-Commodity Pickup-and-Delivery TSP'.

The TIDEL Research Group

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, robotics, supply chain management, vehicle routing, 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 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.

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University of Toronto Mechanical and Information Engineering