Optimize scheduling of human resources and service vehicles - AB-034
Preferred Disciplines: Mathematics, Computer Science (Master, PhD or Post-Doc)
Project length: 4-6 months (1 unit) per intern
Desired start date: As soon as possible
Location: Airdrie, AB
No. of Positions: 2
Preferences: Biggest brains closest to Airdrie. Exact institution we are indifferent; just want to work with the smartest people available.
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
- 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