Optimization of university course scheduling problem with a hybrid artificial bee colony algorithm

Abstract

Course scheduling problem (CSP) is concerned with developing a timetable that illustrates a number of courses assigned to the classrooms. In this study, a hybrid algorithm composed of a heuristic graph node coloring (GNC) algorithm and artificial bee colony (ABC) algorithm is proposed to solve CSP. The study is one of the few applications of ABC on discrete optimization problems and to our best knowledge it is the first application on CSP. A basic heuristic algorithm of node coloring problem takes part initially to develop some feasible solutions of CSP. Those feasible solutions correspond to the food sources in ABC algorithm. The ABC is then is used to improve the feasible solutions. The employed and onlooker bees are directed or controlled in a specific manner in order to avoid the conflicts in the course timetable. Proposed solution procedure is tested using real data from a university in Turkey. The experimental results demonstrate that the proposed hybrid algorithm yields efficient solutions.

School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

“…As a result, the firefly algorithm can effectively solve multi-objective optimisation [45]. Though the ABC algorithm [33] has been widely studied for solving educational timetabling problems, especially solving course timetabling problems [34,49,50] and examination timetabling problems [35,36,51], it has not been applied to STPs. Therefore, this research adapts the ABC algorithm to the proposed problem, which will be introduced in detail in Sections 4.2 and 4.3.…”

Modelling and Solving Course Scheduling Problems (Case Study: Mathematics Study Program, Institut Teknologi Sumatera)

“…In designing a schedule for classes offered on a study program, there are constraints that must be fulfilled and cannot be violated, and those constraints are called hard constraints; for example, a course at time t1 given by lecturer A in room R1, then lecturer A cannot teach at time t1 for other courses and room R1 cannot be used by other lectures at time t1, and so on. In addition to hard constraints, there are some soft constraints [2]. These types of constraints may not be fulfilled.…”

O-BEE-COL: Optimal BEEs for COLoring Graphs

“…The ABC algorithm is a rather recent optimization technique inspired by the intelligent foraging behavior of a colony of bees, whose strength lies in the collective behavior of self-organized swarms that individually behave without any supervision. During the last decade, ABC has attracted quite a number of researchers, and it has been successfully applied mainly to continuous optimization problems [23,3], whilst, rather few works have appeared concerning discrete optimization problems (see, for example, [27,31]). In many cases the results obtained by ABC, including the ones of this work, demonstrate that this metaheuristic is able to compete with, and sometimes even outperforms, existing state-of-the-art algorithms for difficult optimization problems.…”