Memetic Algorithm for a University Course Timetabling Problem

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. The constraints could be hard (encouraged to be satisfied) or soft (better to be fulfilled). This problem becomes complicated for universities which have an immense number of students and lecturers. Moreover, several universities are implementing student sectioning which is a problem of assigning students to classes of a subject while respecting individual student requests along with additional constraints. Such implementation enables students to choose a set of preference classes first then the system will create a timetable depend on their preferences. Subsequently, student sectioning significantly increases the problem complexity. As a result, the number of search spaces grows hugely multiplied by the expansion of students, other variables, and involvement of their constraints. However, current and generic solvers failed to meet scalability requirement for student sectioning UCTP. In this paper, we introduce the Multi-Depth Genetic Algorithm (MDGA) to solve student sectioning UCTP. MDGA uses the multiple stages of GA computation including multi-level mutation and multi-depth constraint consideration. Our research shows that MDGA could produce a feasible timetable for student sectioning problem and get better results than previous works and current UCTP solver. Furthermore, our experiment also shows that MDGA could compete with other UCTP solvers albeit not the best one for the ITC-2007 benchmark dataset.

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A New Sum Coloring-based Integer Linear Programming for Solving the University Course Timetabling Problem

2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA) ◽

10.1109/octa49274.2020.9151838 ◽

2020 ◽

Author(s):

Olfa Harrabi ◽

Jouhaina Chaouachi Siala

Keyword(s):

Linear Programming ◽

Integer Linear Programming ◽

Course Timetabling ◽

Timetabling Problem ◽

University Course Timetabling ◽

Sum Coloring ◽

Course Timetabling Problem ◽

University Course ◽

The University

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The application of Coarse-Grained Parallel Genetic Algorithm with Hadoop in University Intelligent Course-Timetabling System

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v10i8.5206 ◽

2015 ◽

Vol 10 (8) ◽

pp. 11

Author(s):

Liping Wu

Keyword(s):

Genetic Algorithm ◽

Educational Administration ◽

Coarse Grained ◽

Map Reduce ◽

Parallel Genetic Algorithm ◽

Course Timetabling ◽

Timetabling Problem ◽

University Course Timetabling ◽

Course Timetabling Problem ◽

University Course

The university course-timetabling problem is a NP-C problem. The traditional method of arranging course is inefficient, causes a high conflict rate of teacher resource or classroom resource, and is poor satisfaction in students. So it does not meet the requirements of modern university educational administration management. However, parallel genetic algorithm (PGA) not only have the advantages of the traditional genetic algorithm(GA), but also take full advantage of the computing power of parallel computing. It can improve the quality and speed of solving effectively, and have a broad application prospect in solving the problem of university course-timetabling problem. In this paper, based on the cloud computing platform of Hadoop, an improved method of fusing coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is deeply researched, and which is used to solve the problem of university intelligent courses arrangement. The simulation experiment results show that, compared with the traditional genetic algorithm, the coarse-grained parallel genetic algorithm not only improves the efficiency of the course arrangement and the success rate of the course, but also reduces the conflict rate of the course. At the same time, this research makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm, and also solves the problem of university scheduling problem more effectively.

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