Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem

Kenekayoro, Patrick; Zipamone, Godswill

doi:10.9734/bjmcs/2016/23143

Cited by 7 publications

(3 citation statements)

References 18 publications

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“… Provide the number of double lessons requested by each event. [15,20,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]  Classes taught by the same professor  Classes held in the same room  A class cannot be assigned to a particular room unless the capacity of the room is  Balance or spread out the lectures over the week.  Classes may request contiguous time slots.…”

Section: Resultsmentioning

confidence: 99%

A study on optimization methods for solving course timetabling problem in university

Mazlan¹,

Makhtar²,

Mohamed³

et al. 2018

IJET

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Course timetabling is one of the most important processes faced by any educational institution. However, the course timetabling process is time consuming and tiresome as it needs to be done for each regular semester. This paper aims to study on the Optimization methods to solve the course timetabling problem. The study is obtained and discussed by categorizing between the classification of Hard Constraint and Soft Constraint and the classification of Optimization Methods. From the study, it shows that Meta-Heuristics are the mostly method used in solving the course timetabling problem. It is concluded that this method is suitable for future used compared to other techniques studied. An analysis and observation will be carried out for the research future.

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Section: Resultsmentioning

confidence: 99%

A study on optimization methods for solving course timetabling problem in university

Mazlan¹,

Makhtar²,

Mohamed³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…Under the umbrella of bio-inspired algorithms, the popular ones are the particle swarm optimisation [42] (PSO) algorithm, the ant colony optimisation (ACO) [43], the artificial fish swarm algorithm [44] (AFS), the whale optimisation algorithm [40] (WOA), the firefly algorithm [45], the shuffled frog leaping (SFL) [46] algorithm and the artificial bee colony (ABC) [33] algorithm. The PSO algorithm was introduced in 1995 by Kennedy and Eberhart [42], inspired by the food-searching behaviour of bird flocks.…”

Section: Related Bio-inspired Optimisation Methodsmentioning

confidence: 99%

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

Zhu

2021

Mathematics

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Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators’ availabilities, preferences, and expertise levels.

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“…Meta-heuristic and hyper-heuristic approaches are methods of high level which are used to find the solution to problems with a large computational complexity. For instance, such are: "tabu search" [16]; "simulated annealing" [17]; "variable neighborhood search" [18] and "ant colony optimization" [19].…”

Section: Meta-heuristic and Hyper-heuristic Approachesmentioning

confidence: 99%

A Modified Event Grouping Based Algorithm for The University Course Timetabling Problem

Kralev

Kraleva

Kumar

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper presents the study of an event grouping based algorithm for a university course timetabling problem. Several publications which discuss the problem and some approaches for its solution are analyzed. The grouping of events in groups with an equal number of events in each group is not applicable to all input data sets. For this reason, a universal approach to all possible groupings of events in commensurate in size groups is proposed here. Also, an implementation of an algorithm based on this approach is presented. The methodology, conditions and the objectives of the experiment are described. The experimental results are analyzed and the ensuing conclusions are stated. The future guidelines for further research are formulated.

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Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem

Cited by 7 publications

References 18 publications

A study on optimization methods for solving course timetabling problem in university

A study on optimization methods for solving course timetabling problem in university

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

A Modified Event Grouping Based Algorithm for The University Course Timetabling Problem

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