Worker scheduling with induced learning in a semi-on-line setting

Date of Award


Publication Type

Master Thesis

Degree Name



Industrial and Manufacturing Systems Engineering

First Advisor

Zhang, Guoqing (Industrial & Manufacturing Systems Engineering)


Engineering, Industrial.



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


Scheduling is a widely researched area with many interesting fields. The presented research deals with a maintenance area in which preventative maintenance and emergency jobs enter the system. Each job has varying processing time and must be scheduled. Through learning the operators are able to expand their knowledge which enables them to accomplish more tasks in a limited time. Two MINLP models have been presented, one for preventative maintenance jobs alone, and another including emergency jobs. The emergency model is semi-on-line as the arrival time is unknown. A corresponding heuristic method has also been developed to decrease the computational time of the MINLP models. The models and heuristic were tested in several areas to determine their flexibility. It has been demonstrated that the inclusion of learning has greatly improved the efficiency of the workers and of the system.