Auto-Generate Scheduling System Based on Expert System *Nur Iqtiyani Ilham, E. H. Mat Saat, N. H. Abdul Rahman, Farah Yasmin Abdul Rahman, Nurhani Kasuan Faculty of Electrical Engineering Universiti Teknologi MARA (UiTM) Masai, 81750 Johor, Malaysia [email protected] Abstract — This paper proposed a technique for smart auto- generate scheduling system specifically for the educational sector. In constructing a precise and high efficient timetable there are constraints that needs to be conceded i.e. availability of class rooms, students, lecturers, courses, time slots etc. These are the tedious elements that contribute to the challenges in producing the same. Considering Faculty of Electrical Engineering (FKE) Universiti Teknologi MARA (UiTM), Pasir Gudang campus as a piloted project, the proposed Auto-Generated Scheduling System (AGSS) is expected to overwhelm these problems. AGSS will provide the accessibility to the timetable committee to arrange the detail by simply loaded the information i.e. numbers of lecturer, list of class room, courses and loading detail (ATS) into the developed algorithm Artificial Intelligence (AI) expert system. Xampp and Visual basic is used in developing the timetable database and Graphical User Interface (GUI) for timetable system respectively. Based on the loaded information, the system will generate the class timetable automatically with individual user customizable setting. AGSS is adept to envisage the cost effective with fast and precise solution on the timetable management thus providing alternative solutions for timetable management while maintaining quality, reliability, and functionality . Keywords—expert system; auto-generate; visual basic; timetable system; smart scheduling; I. I NTRODUCTION Initially, prior to the enrolment of a new semester, the timetable committee of UiTM Pasir Gudang have to arrange manually the specific requirement input data (i.e. lecturer details, courses, classes and time slot) into their existing scheduling system. UiTM for instance is applying Smart Scheduling System (SSS) to generate manually the class timetable. This hoary method entails inefficiency and time consuming due to the cases happened whereby the timetable need to rearrange several times due to unexpected changes on the parameters details. It is essential to ensure the precision of generated timetable to evade any discrepancy and failure on the scheduling system. Though various approaches automated system are available to solve the timetable management problem, however, most of the organizations/universities still endure to solve the problem manually. This is happened due to most of the available systems are yet to provide additional features for customaries to furnish users special needs [1, 2]. In dealing with the timetables management, different optimization methods (i.e. particle swarm optimization (PSO), genetic algorithm (GA), tabu search (TS), ant colony system (ACS), fuzzy logic, Simulated Annealing (SA) etc.) have been used to obtain fast-
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- Spring '17
- Artificial Intelligence, Ant colony optimization, Lecturer, TABU Search