A Hybrid Genetic Algorithm for Course Scheduling and Teaching Workload Management

Matias, Junrie B.; Fajardo, Arnel C.; Medina, Ruji P.

doi:10.1109/hnicem.2018.8666332

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…Traditional course scheduling is a tedious and complex work, and the courses of electronic engineering specialty are relatively complex, which increases the difficulty of course scheduling. It is not only necessary to make reasonable organization and arrangement for students, teachers, and curriculum specialty but also under the continuous expansion and development of the scale of Electronic Engineering students every year, the education of electronic engineering specialty becomes tenser and tenser and manual course scheduling cannot meet the teaching status of electronic engineering specialty [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Design of Automatic Course Arrangement System for Electronic Engineering Teaching Based on Monte Carlo Genetic Algorithm

Shuai

2021

Security and Communication Networks

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In order to overcome the problems of convergence and low satisfaction in the traditional course scheduling system, a new Electronic Engineering Teaching Automatic Course Scheduling System based on the Monte Carlo genetic algorithm is proposed in this paper. The overall structure and hardware structure of the course scheduling system are designed. The hardware includes system management, course scheduling information input, course scheduling management, and course schedule query. In the software part, the Monte Carlo genetic algorithm is used to optimize the course scheduling optimization process, and a course scheduling scheme more in line with the needs of students and teachers is obtained. The experimental results show that the Monte Carlo genetic algorithm has higher convergence and higher user satisfaction compared with the traditional genetic algorithm. Therefore, it shows that the performance of the course scheduling system has been effectively improved.

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Section: Introductionmentioning

confidence: 99%

Design of Automatic Course Arrangement System for Electronic Engineering Teaching Based on Monte Carlo Genetic Algorithm

Shuai

2021

Security and Communication Networks

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“…While smaller instances might be solved by exact algorithms, most real-world problems are large dimensional problems, so there is a need for heuristic methods to obtain near-optimal solutions in reasonable time. The most used ones are metaheuristics like tabu search (Valdes, Crespo and Tamarit, 2002;Yuan and Lan, 2016), simulated annealing (Abramson, 1991;Thompson and Dowsland, 1998;Bellio, Ceschia, Di Gaspero, Schaerf and Urli, 2016;Goh, Kendall and Sabar 2018), genetic and evolutionary algorithms (Beligiannis, Moschopoulosa, Kaperonisa and Likothanassisa, 2008;Susan and Bhutani, 2018;Matias, Fajardo and Medina, 2018), neural networks (Kovačič, 1993), ant colonies (Socha et al, 2003), bee colony algorithm (Bolaji, Kahader and Betar, 2014), particle swarm optimization (Chen and Shih, 2013;Imran Hossain, Akhand, Shuvo, Siddique and Adeli,, 2019), artificial immune algorithm (Yazdani, Naderi and Zeinali, 2017) and hyperheuristics (Burke, McCollum, Meisels, Petrovic, and Qu, 2007b). Besides, there are some studies dealing with the analysis and design of interactive decision support system for timetable management (Piechowiak and Kolski, 2004;Kamisli Ozturk, Ozturk and Sagir, 2010).…”

Section: Problem Complexity and The Need For A Heuristic Approachmentioning

confidence: 99%

A New Mathematical Model and Random Key Based Metaheuristic Solution Approach for Course-Room-Time Assignment Problem

Öztürk

Sagir

2019

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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Course-room-time assignment problem, Mathematical modelling, Random key based genetic algorithms.This study presents a newly developed mixed-integer mathematical model for university course-room-time assignment problem. Optimal results with no soft constraint violations are obtained for some type of problem instances. As problem complexity increases it becomes more difficult to find feasible solution for this problem in a reasonable time. Therefore, a heuristic approach is often needed for such problems. In this study, a random key based genetic algorithm (RKGA) is developed. RKGA encoding is used in order to encode the chromosomes with a length of just the number of courses and not to use problem specific genetic operators and/or repair mechanisms. Well-known problem instances from the literature are selected to evaluate the outcome. The performance of RKGA is competitive to that of other algorithms especially for big size problems. Öz Ders-derslik-zaman dilimi atama problemi, Matematiksel modelleme, Rassal anahtar temelli genetik algoritma.Bu çalışmada üniversite ders-derslik-zaman dilimi atama problemi için yeni bir karma tam sayılı matematiksel model önerilmiştir. Geliştirilen karma tam sayılı matematiksel model ile literatürde yer alan test problemleri çözdürülmüş ve bir kısmı için tüm esnek kısıtlar sağlanarak en iyi çözüm elde edilmiştir. Problem karmaşıklığı arttıkça makul sürelerde uygun çözüm bulmak zorlaştığından, bu tür problemlerin çözümü için sezgisel bir yaklaşıma ihtiyaç duyulmaktadır. Çalışmada, rassal anahtar temelli bir genetik algoritma (RKGA) geliştirilmiştir. Probleme özgü özel genetik operatörler ve/veya onarma mekanizmaları kullanmamak için sadece ders sayısı uzunluğundaki kromozomları kodlamak için RKGA kodlaması kullanılmıştır. Çıktıların değerlendirilmesi için literatürde iyi bilinen test problemleri seçilmiştir. Özellikle büyük boyutlu problemlerde RKGA'nın performansının diğer algoritmalar ile rekabet edebilir düzeyde olduğu görülmüştür. Araştırma Makalesi defined as assigning courses to rooms and timeslots in such a way that there are no conflicts or clashes by satisfying objectives and the examination timetabling basically involves allocating a set of examinations to a set of rooms and time periods. The basic difference is that in course timetabling there cannot be more than one course per room in contrary of exam one. Since 1960's, when the timetabling problems have Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Sets and indices J = {j | j=1,…, n} for courses I = {i | i =1,…, m} for students K = {k | k = 1,…, h} for rooms L = {l | l = 1,… , o} for features T = {t | t =1,…, 45 } for timeslots D = {d | d =1,…, 5 } for days Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık

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“…Genetic algorithms are quite often used for creating automatized solutions to solve timetable creation challenge. Some of different models based on genetic algorithms are proposed in following papers: [17], [18], [19], [24] [13].…”

Section: Support To the Academic Staffmentioning

confidence: 99%

Application of Genetic Algorithms in Higher Education Area

Kučak¹,

Juričić²,

Đambić³

2019

DAAAM Proceedings

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In computer science, a genetic algorithm (GA) is a computational method for solving optimization problems. Genetic algorithms are inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. The application of genetic algorithms in higher education area is the focus of our study. The aim of this paper is to evaluate the possibilities of applying genetic algorithms in the higher education area. This paper identifies and analyses suitable literature, research papers and articles in order to determine their categorization in the field of higher education, to determine the current trends of using genetic algorithms in education, and to determine its current and future applications.

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A Hybrid Genetic Algorithm for Course Scheduling and Teaching Workload Management

Cited by 10 publications

References 19 publications

Design of Automatic Course Arrangement System for Electronic Engineering Teaching Based on Monte Carlo Genetic Algorithm

Design of Automatic Course Arrangement System for Electronic Engineering Teaching Based on Monte Carlo Genetic Algorithm

A New Mathematical Model and Random Key Based Metaheuristic Solution Approach for Course-Room-Time Assignment Problem

Application of Genetic Algorithms in Higher Education Area

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