Optimize scheduling of human resources and service vehicles - AB-034
Preferred Disciplines: Master or better in Math / Computer Science
Project length: 4-6 months (1 unit) per intern
Desired start date: ASAP – company is ready to start immediately
Location: Airdrie, Alberta
No. of Positions: 2
Preferences: Biggest brains closest to Airdrie. Exact institution we are indifferent; just want to work with the smartest people available. Language: no preference
National fleet of service technicians working coast to coast to coast. Servicing hundreds of thousands of assets across thousands of locations for hundreds of customers.
Organisation needs to route service vehicles across Canada on multi-day service trips; typically a 5 day to 15 day trip, servicing 1 to 12 different customers across 1 to 50 different job locations.
- Different service types
- Different human skills / certifications available
- Different service vehicles available
- hundreds of thousands of service locations in Canada
- thousands of service orders fulfilled annually
Not an easy puzzle
Lots of puzzles, caveats and constraints
Background and required skills
- Objective is to apply deep learning / machine learning using historical trip data from the last 10 years to better identify efficiencies, rulesets and to ultimately use artificial intelligence for all future scheduling of human resources.
- Build artificial intelligence to replace human scheduling process
- Rapid Experimental Development
- Many Iterations and Revisions
Expertise and Skills Needed:
- Deep learning techniques / understanding
- Algorithmic analysis / processing
- Software development
- Statistical analysis
- All work is done on Ubuntu / Linux computers and servers
For more info or to apply to this applied research position, please
- Check your eligibility and find more information about open projects.
- Complete this webform. You will be asked to upload your CV. Remember to indicate the title of the project(s) you are interested in and obtain your professor’s approval to proceed!
Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform or directly to Oba Harding at, oharding(a)mitacs.ca.