According to several research studies conducted on timetable scheduling from the '90s to the present, it has used technologies such as Operational Methods, Human-Machine Interaction and Artificial Intelligence (AI). Over the years, researchers have used a variety of ways to develop ‘optimal’ timetabling solutions based on a set of criteria. According to Dennise Adrianto’s (2013)[1] research, AI may be defined as a system capable of completing transactions with the help of ‘schedule’ scheduling. Regardless of the technology employed, the most frequent method of scheduling timetables is to apply limits and schedule resources accordingly. When it comes to collecting hard and soft constraints in various settings, all of these methods have varied efficiency. Time table management approaches use fast computing abilities on different optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Tabu Search (TS), Ant Colony System (ACS), Fuzzy Logic (FL), Simulated Annealing (SA), etc are used along with the different algorithm types like Metaheuristic and Memetic Algorithms.[2]
A research approach named Automated Scheduling System for University Lectures and Examinations, conducted by Arikpo and Okokon (2018)[3], mentions a basic strategy based on Genetic Algorithms (GA) and Evolutionary Algorithms (EA). As a Genetic The problem will be represented in a sequence of bit strings modified by the algorithm, while in Evolutionary Algorithms, the decision variables and problem functions will be employed directly. A mathematical programming model for faculty lecture assignments is established in this study, with each variable representing a full instructor schedule and the issue phrased as a sequence of partitioning problems with side constraints. The approach to system design is based on Object-Oriented Analysis and Design (OOAD). The primary users of the system are department admin, students or lecturers and faculty admin. The department admins can access the cases such as scheduling lectures, registering courses, venues and changing the credentials of users, while faculty admins can register for a department and venues. On the other hand, students and lecturers can only view the timetable. Because they express numerous timetabling difficulties formally and propose diverse solutions for them, this methodology falls within the graph colouring method outlined above. Arikpo and Okokon (2018)[3] used an object-oriented analysis and design method, employing the unified modelling language and Java Enterprise Edition version 6 as the backend database system. They could generate a timetable that resolved lecture room and time issues using random lecture schedules while still satisfying their constraints.
As Ilham, Saat and Rahman (2017)[4] mentioned in their research on Auto-Generate Scheduling systems based on expert systems, Particle Swarm Optimization (PSO) is a population-based algorithm but possesses poor local optima which affects its global searching abilities. So, they suggest hybrid methods such as Particle Swarm Optimization with Tabu Search to improve efficiency and accuracy. The proposed Auto-Generated Scheduling System (AGSS) by Ilham, Saat and Rahman (2017)[4] is an Artificial Intelligence Expert System in which the information number of lecturers, and list of lecture rooms and courses are input to generate a timetable automatically. This AGSS method can be named an AI-based expert system with user customisation and a user-friendly User Interface (UI) to develop an auto-generated timetable. First, the system is fed with the lecture lists, lecture room lists with the special requests and user customization settings through bridging the database, the expert Al system will output the lecturer’s timetable, student's timetable, course timetable and lecture room timetables separately. The main coding language used here is Visual Basic to develop AGSS and GUI. Another research carried out by Dennise Adrianto (2013)[1] a comparison Using Particle Swarm Optimization and Genetic Algorithm for Automatic Timetable Scheduling by using another method of Artificial intelligence named Particle Swarm Optimization (PSO). In PSO, the solution for an optimization problem is sought by using several particles (solutions) that are constituted with swarms moving around the searching place looking for the best solution. Simply, it is a computational method which optimises the problem based on the movement and intelligence of swamps, iteratively trying to create a solution. It’s been concluded that timetable scheduling can be efficiently done through human and computer interaction with the methods of computation and AI. The studies have found few primary as well as secondary methods including AI and computation, such as Sequential Methods, Cluster Methods, Constraint-Based Methods and Metaheuristic Methods.