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1st and 2 nd Generation of population from Parents.
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... Since 1980s, the solutions to the timetabling problems have been proposed [1]. However, the research in this area is still active as it is still difficult to find an effective general solution in timetabling due to the variety of constraints and requirements of different institutes as can be found in [2][3][4][5][6][7][8][9][10]. Different techniques have been applied in optimizing university timetable such as data mining [4], rule-based approaches and memetic algorithm [5], as well as genetic algorithm (GA) [6]. ...
Currently, there are several drawbacks in the existing university timetable system, including students do not
know the class hour of subjects that they are going to register during the registration period. This situation has resulting in
clashing in the class hour of the registered subjects. In addition, some students are not aware of this situation until it is too
late from them to drop and register other subjects. This particularly happened to those who has more than two registered
subjects clash and resulting in overlook at the rest of clashed subjects. Besides, students find it is inconvenient to view the
timetable through the current timetable system. Thus, the development of an Android based mobile application that able
to notify students if the class hour of the subjects they registered are overlapping is presented in this paper. The application
also able to manage the student timetable by reporting details such as venue, day and the time of clashed subjects. Then,
the application will suggest student to select elective subjects or subjects that students wish to retake to prevent clashing
in class hour. The software development of the application is based on the Agile model and using rule-based algorithm.
The application is developed in Android Studio by using Java as the programming language and all data is stored in
Firebase Realtime Database. In conclusion, this paper presents results of the application that has successfully designed and
developed.
... These constraints, which are rules, policies, and preferences of universities, lecturers, and students, can be categorized as hard, soft, or medium. Hard constraints are those guidelines that should not be violated or overlapped at all, soft constraints are desires of the involved stakeholders that can be ignored without any serious consequences, and medium constraints are preferences that they are preferred to not be violated [3]. The committee handling the timetabling must consider all these requirements to create an optimal outcome. ...
The university course timetabling problem looks for the best schedule, to satisfy given criteria as a set of given resources, which may contain lecturers, groups of students, classrooms, or laboratories. Developing a timetable is a fundamental requirement for the healthy functioning of all educational and administrative parts of an academic institution. However, factors such as the availability of hours, the number of subjects, and the allocation of teachers make the timetable problem very complex. This study intends to review several optimization algorithms that could be applied as possible solutions for the university student course timetable problem. The reviewed algorithms take into account the demands of institutional constraints for course timetable management.
У статті розглянуто питання впровадження цифрових та інформаційних технологій для покращення організації учбового процесу у закладах вищої освіти, зокрема розробка та використання програмних засобів, що можуть використовуватися у мобільних пристроях на прикладі розробки Android-застосунку розкладу занять університету. У відповідності до мети, проведено аналіз потреб студентів та викладачів по відношенню до такої інформаційної платформи, виконано обґрунтування вибору операційної системи та середовища розробки мобільного додатку. Запропоновано алгоритм роботи мобільного застосунку, який забезпечує зручний доступ до розкладу занять з можливістю фільтрації даних, що будуть відображені на екрані та можливі зміни яких будуть у подальшому відслідковуватися програмою у автоматичному режимі. Також врахована реалізація додаткових функцій по сповіщенню користувача про зміни розкладу, та завчасне інформування про початок наступної пари. Зроблено висновки про доцільність отримання відгуків від користувачів після впровадження застосунку, а також окреслені шляхи можливого вдосконалення цього мобільного застосунку.
This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018.
