Online Job Scheduling and Risk Handling in Job Queues

This project is concerns the development of a real-time computational, probabilistic algorithm or artificial intelligence to predict when a scheduled job in a mobile workers job queue is at risk, given the type of jobs in the queue and the historical durations to complete them. In addition, consideration must be given to traffic patterns, and time-in-transit that the worker is likely to face moving between jobs. When a job is identified as at-risk, the algorithm should find the best alternative worker to assign the job, and place it in that drivers job queue at an optimal position. WebTach will benefit from the proposed research by gaining algorithms, methods and software for solving online scheduling problems in real applications.

Faculty Supervisor:

Quianping Gu

Student:

Partner:

Discipline:

Computer science

Sector:

Manufacturing

University:

Simon Fraser University

Program:

Accelerate