scholarly journals Developing a Model for the University Course Timetabling Problem: A Case Study

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mozhgan Mokhtari ◽  
Majid Vaziri Sarashk ◽  
Milad Asadpour ◽  
Nadia Saeidi ◽  
Omid Boyer
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
University Course Timetabling ◽  
Proposed Model ◽  
Engineering Department ◽  
Academic Settings ◽  
Teachers And Students ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University

Over recent years, timetable programming in academic settings has become particularly challenging due to such factors as the growing number of students, the variety of lectures, the inadequacy of educational facilities in some areas, and the incorporation of teachers and students’ preferences into the schedule. Many researchers, therefore, have been formulating the problem of timetabling lectures using different methods. In this research, a multiobjective mixed-integer programming model was developed to provide a timetable for the postgraduate courses at the Industrial Engineering Department of Islamic Azad University, Najafabad Branch (IAUN). The proposed model minimized the violation of the lecturers and educational priorities, the student travel time between classes, and the classes’ surplus capacity. To convert the multiobjective model into a single one, the ε-constraint method was adopted, and the model’s accuracy and feasibility were examined through a real example solved by the CPLEX solver of GAMS software. The results approved the efficiency of this model in preparing a timetable for university lectures.

scholarly journals Considering Section Balance in an Integer Optimization Model for the Curriculum-Based Course Timetabling Problem

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1763
Author(s):  
Cristian D. Palma ◽  
Patrick Bornhardt
Keyword(s):  
Programming Model ◽  
Educational Institution ◽  
Integer Optimization ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Proposed Model ◽  
Actual Solution ◽  
Course Timetabling Problem ◽  
University Course

University course timetabling is a complex and time-consuming duty that every educational institution faces regularly. It consists of scheduling a set of lectures in predefined time slots so as to avoid student conflicts, meet teacher and room availability, and manage several institution-specific operational rules. In this paper, we schedule courses based on a curriculum, that is, before the students’ registration. Unlike other curriculum-based models, the proposed model considers two practical aspects when managing the conflicts between lectures: (i) it schedules sections of subjects so that each section is evenly likely to be registered by the students, and (ii) it considers the failure rates and periodicity a subject is taught. We present a multi-objective integer programming model that maximizes the use of specific time slots, the symmetry in which the lectures of a course are scheduled during a week, and the flexibility for straggler students to take courses. The model is solved using commercial software, and it is applied to a real course-timetabling problem. We show the advantages of its use by comparing the model’s solution with the actual solution obtained by the manual scheduling.

scholarly journals University Course Timetabling Problem with Professor Assignment

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Nancy Maribel Arratia-Martinez ◽  
Cristina Maya-Padron ◽  
Paulina A. Avila-Torres
Keyword(s):  
Programming Model ◽  
Fixed Number ◽  
Full Time ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Part Time ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University

One of the decision problems in many organizations and institutions is to decide how to schedule different tasks, in particular, in higher education institutions. One of the main problems is the university course timetabling problem (UCTP): this problem consists of the allocation of events (courses, professors, and students) to a number of fixed time slots and rooms, this at the beginning of each academic period of the universities. The existent formulations include particular requirements from different educational levels and institutions, as in our case. In this paper, we focus on the university course timetabling problem with the assignment of professor-course-time slot for an institution in Mexico. Timetabling is constructed for the disciplinary courses that are offered by one of the academic departments. The main characteristics are as follows: (1) there are full-time and part-time professors; (2) a mandatory fixed number of courses has to be assigned to each full-time professor according to their academic profile; (3) there is a maximum number of courses assigned to part-time professors; (4) a professor-course matrix that specifies the valid assignation is defined; and (5) mandatory time periods for courses in different semesters are established and other traditional constraints. We present the integer linear programming model proposed to solve the case studied. The optimal solution was obtained with low computational effort through the classical branch-and-bound algorithm. We describe the complete timetable to show the model effectiveness.

scholarly journals Solving a Real Constraint Satisfaction Model for the University Course Timetabling Problem: A Case Study

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Marco Antonio Cruz-Chávez ◽  
Mireya Flores-Pichardo ◽  
Alina Martínez-Oropeza ◽  
Pedro Moreno-Bernal ◽  
Martín H. Cruz-Rosales
Keyword(s):  
Constraint Satisfaction ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Proposed Model ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University ◽  
Classroom Use ◽  
Satisfaction Model

This paper proposes a real mathematical constraint satisfaction model which defines the timetabling problem in the Faculty of Chemical Sciences and Engineering (FCSE) at the Autonomous University of Morelos State, Mexico. A Constructive Approach Algorithm (CAA) is used to obtain solutions in the proposed model. A comparison is made between the CAA’s results and the schedule generated by the FCSE administration. Using the constraint satisfaction model, it is possible to improve the allocation of class hours in the FCSE so that classroom use is more efficient.

scholarly journals Solving University Course Timetabling Problem Using Multi-Depth Genetic Algorithm

2020 ◽  
Vol 77 ◽  
pp. 01001
Author(s):  
Alfian Akbar Gozali ◽  
Shigeru Fujimura
Keyword(s):  
Genetic Algorithm ◽  
Individual Student ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Search Spaces ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University ◽  
Additional Constraints

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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