Аннотации:
The current expansion of national colleges and universities or the
increase in the number of enrolments requires teaching management to ensure
the quality of teaching. The problem of scheduling is a very complicated problem in teaching management, and there are many restrictions. If the number of
courses scheduled is large, it will be necessary to repeat the experiment and
make adjustments. This kind of work is difficult to accomplish accurately by
manpower. Moreover, for a comprehensive university, there are many subjects,
many professional settings, limited classroom resources, limited multimedia
classroom resources, and other factors that limit and constrain the results of
class scheduling. Such a large data volume and complicated workforce are difficult to complete accurately. Therefore, manpower scheduling cannot meet the
needs of the educational administration of colleges and universities. Today,
computer technology is highly developed. It is very economical to use software
technology to design a course scheduling system and let the computer complete
this demanding and rigorous work. Common course scheduling systems mainly
include hill climbing algorithms, tabu search algorithms, ant colony algorithms,
and simulated annealing algorithms. These algorithms have certain shortcomings. In this research, we investigated the mutation genetic algorithm and
applied the algorithm to the student’s scheduling system. Finally, we tested the
running speed and accuracy of the system. We found that the algorithm worked
well in the course scheduling system and provided strong support for solving
the tedious scheduling work of the educational administration staff.
