University Course Scheduling Using Evolutionary Algorithms

2009 ◽  
Author(s):  
Mohammed Aldasht ◽  
Mahmoud Alsaheb ◽  
Safa Adi ◽  
Mohammad Abu Qopita
Keyword(s):  
Evolutionary Algorithms ◽  
Course Scheduling ◽  
University Course

A Constraint-Based Dental School Timetabling System

AI Magazine ◽  
2014 ◽  
Vol 35 (1) ◽  
pp. 53
Author(s):  
Hadrien Cambazard ◽  
Barry O'Sullivan ◽  
Helmut Simonis
Keyword(s):  
University Hospital ◽  
Dental School ◽  
Automated System ◽  
Mixed Integer ◽  
Analysis Tool ◽  
Course Scheduling ◽  
Java Application ◽  
School Timetabling ◽  
Programming Solution ◽  
University Course

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This sy stem has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase the number of students enrolled to the maximum possible given the available resources. It also provides the school with a valuable “what-if” analysis tool.

scholarly journals Implementasi Algoritma Kunang-Kunang Untuk Penjadwalan Mata Kuliah di Universitas Ma Chung

2015 ◽  
Vol 6 (4) ◽  
Author(s):  
Hendry Setiawan ◽  
Lo Hanjaya Hanafi ◽  
Kestrilia Rega Prilianti
Keyword(s):  
Light Intensity ◽  
Resource Sharing ◽  
Firefly Algorithm ◽  
Course Scheduling ◽  
S Parameters ◽  
Lower Light ◽  
University Course ◽  
Complex Matter ◽  
Lower Light Intensity

Abstract. Course scheduling is considered as a complex matter because the generated schedule must guarantee that there are no clashes of classes, lecturers, and students’ schedules. At Ma Chung University, course scheduling is still accomplished manually. Due to the limited number of rooms and lecturers r, resource sharing system is applied. This causes complication in manual scheduling. Firefly algorithm is implemented in this application to schedule the course automatically. A schedule solution is represented as a firefly. Firefly with lower light intensity will move toward firefly with higher light intensity, so that a better solution is found. Based on a scheduling test, the best light intensity value of firefly is reached when firefly algorithm’s parameters, β0 and γ, are given 1 and 10 with light intensity value of 0,0003831. Keywords: course, firefly algorithm, scheduling  Abstrak. Penjadwalan mata kuliah merupakan hal yang kompleks karena jadwal yang dihasilkan tidak hanya menjamin jadwal pertemuan semua kelas dan dosen tidak bentrok, tetapi juga menjamin jadwal pertemuan semua mahasiswa tidak bentrok. Penjadwalan mata kuliah di Universitas Ma Chung masih dilakukan secara manual. Karena jumlah kelas dan dosen yang dimiliki terbatas, maka diterapkan sistem resource sharing. Sistem resource sharing ini membuat proses penjadwalan yang dilakukan secara manual menjadi lebih rumit. Algoritma yang digunakan untuk penjadwalan mata kuliah pada aplikasi ini adalah algoritma kunang-kunang. Sebuah solusi jadwal mata kuliah dalam algoritma kunang-kunang direpresentasikan sebagai seekor kunang-kunang. Kunang-kunang dengan intensitas cahaya yang lebih rendah akan bergerak menuju kunang-kunang yang lebih terang sehingga mampu didapatkan solusi jadwal mata kuliah yang lebih baik. Berdasarkan hasil uji coba, nilai intensitas cahaya terbaik didapatkan ketika parameter algoritma kunang-kunang, β0 dimasukkan 1 dan γ dimasukkan 10 hingga didapatkan intensitas sebesar 0,0003831. Kata Kunci: algoritma kunang-kunang, mata kuliah, penjadwalan

scholarly journals University Course Scheduling and Classroom Assignment

2014 ◽  
Vol 18 (1) ◽  
Author(s):  
Camilo Torres-Ovalle ◽  
Jairo Rafael Montoya-Torres ◽  
Carlos Quintero-Araujo ◽  
Angelica Sarmiento Lepesqueur ◽  
Monica Castilla Luna
Keyword(s):  
Course Scheduling ◽  
University Course ◽  
Classroom Assignment

scholarly journals Penerapan Metode Hybrid Genetic Algorithm (GA) dan Pattern Search (PS) untuk Penjadwalan Mata Kuliah Universitas

2020 ◽  
Vol 9 (3) ◽  
pp. 201-212
Author(s):  
Fani Puspitasari ◽  
Parwadi Moengin
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Pattern Search ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Course Scheduling ◽  
Fitness Value ◽  
Optimum Solution ◽  
University Course

The problem of university course scheduling is a complicated job to do because of the many constraints that must be considered, such as the number of courses, the number of rooms available, the number of students, lecturer preferences, and time slots. The more courses that will be scheduled, the scheduling problem becomes more complex to solve. Therefore, it is necessary to set an automatic course schedule based on optimization method. The aim of this research is to gain an optimal solution in the form of schedule in order to decrease the number of clashed courses, optimize room utilization and consider the preferences of lecturer-course. In this research, a hybridization method of Genetic Algorithm (GA) and Pattern Search (PS) is investigated for solving university course scheduling problems. The main algorithm is GA to find the global optimum solution, while the PS algorithm is used to find the local optimum solution that is difficult to obtain by the GA method. The simulation results with 93 courses show that the Hybrid GA-PS method works better than does the GA method without hybrid, as evidenced by the better fitness value of the hybrid GA-PS method which is -3528.62 and 99.24% of the solutions achieved. While the GA method without hybrid is only able to reach a solution of around 65% and has an average fitness value of -3100.76.

scholarly journals University Course Scheduling using the Evolutionary Algorithm

2011 ◽  
Vol 1 (1) ◽  
pp. 7
Author(s):  
Ade Jamal
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Hill Climbing ◽  
Scheduling Problem ◽  
Search Technique ◽  
Academic Administrator ◽  
The Past ◽  
Course Scheduling ◽  
Initial Work ◽  
University Course

Course scheduling problem is hard and time-consuming to solve which is commonly faced by academic administrator at least two times every year. This problem can be solved using search and optimization technique with many constraints. This problem has been well studied in the past, and still becomes favorite subject for researchers. We will briefly discuss the convergence difficulty in our initial work on this subject using a modified hill-climbing search technique[8].  In this paper, an evolutionary algorithm is applied to solve the course scheduling problem and studying mutation techniques involved in the algorithm.

University Course Arrangement System Based on Improved Ant Colony Algorithm Design

2014 ◽  
Vol 651-653 ◽  
pp. 2536-2540
Author(s):  
Xiang Feng Suo ◽  
Yun Hui Gao ◽  
Xue Han
Keyword(s):  
Ant Colony Algorithm ◽  
Algorithm Design ◽  
Ant Colony ◽  
Related Data ◽  
Advantages And Disadvantages ◽  
Course Scheduling ◽  
Data Tables ◽  
University Course ◽  
The University ◽  
Improved Ant Colony Algorithm

This paper starts from learning the basic ant colony algorithm, studies the working principle of ant colony algorithm, summarizes the advantages and disadvantages of ant colony algorithm .And it proposed artificial improved ant colony algorithm based on the basic ant colony algorithm, the improved ant colony algorithm is more suitable for the university experimental class course scheduling, and carries on the overall analysis and detailed research on the actual timetabling problem, find out the reasons of conflict prone to course scheduling, according to the actual situation to solve, and finally find a comparison based on the algorithm of reasonable arrangement of ant colony algorithm, based on the algorithms require the creation of database related data tables, draw flow chart of relevant, according to the above data for design and development of the system.

scholarly journals Modifying Regeneration Mutation and Hybridising Clonal Selection for Evolutionary Algorithms Based Timetabling Tool

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Thatchai Thepphakorn ◽  
Pupong Pongcharoen ◽  
Chris Hicks
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Clonal Selection ◽  
Fractional Factorial ◽  
Hill Climbing ◽  
Clonal Selection Algorithm ◽  
Scheduling Problems ◽  
Genetic Operators ◽  
Course Scheduling ◽  
Elitist Strategy

This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research developed a new approach which (i) modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population; (ii) included the clonal selection algorithm (CSA); and the elitist strategy (ES) to improve the quality of the solutions produced. This approach was adopted by both the GA and MA within the EAT. The MA was further modified to include hill climbing local search. The EAT program was tested using 14 benchmark timetabling problems from the literature using a sequential experimental design, which included a fractional factorial screening experiment. Experiments were conducted to (i) test the performance of the proposed modified algorithms; (ii) identify which factors and interactions were statistically significant; (iii) identify appropriate parameters for the GA and MA; and (iv) compare the performance of the various hybrid algorithms. The genetic algorithm with modified genetic operators produced an average improvement of over 50%.

Stochastic Optimisation Timetabling Tool for university course scheduling

2008 ◽  
Vol 112 (2) ◽  
pp. 903-918 ◽  
Author(s):  
P. Pongcharoen ◽  
W. Promtet ◽  
P. Yenradee ◽  
C. Hicks
Keyword(s):  
Stochastic Optimisation ◽  
Course Scheduling ◽  
University Course
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