Personnel scheduling problem with respect to the workforce demand and rostering has been a subject of research for a long time. Due to the high complexity of the problem, the optimal solution cannot be found in a reasonable amount of time. That is why new methods or their combinations are suggested for automatic scheduling with respect to the known set of constraints. Obviously, employers want employees to perform as many tasks as possible during their working time. On the other hand, they pay attention to the employee’s requests by suggesting flexible working hours, desirable sequence pattern of working days and work time. Optimization with a large set of constraints is not practical if a combination of evolutionary algorithms with the greedy approach is applied. It is planned to design a machine learning based algorithm and implement its prototype during this project. This algorithm would be based on extracting the favourable patterns in the schedule (roster combinations) and using them in the later calculations.
KTU Science and Innovation Fund
Period of project implementation: 2020-04-14 - 2020-12-31