scholarly journals Modeling and Solving a Latin American University Course Timetabling Problem Instance

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1833
Author(s):  
Oscar Chávez-Bosquez ◽  
José Hernández-Torruco ◽  
Betania Hernández-Ocaña ◽  
Juana Canul-Reich
Keyword(s):  
Tabu Search ◽  
Latin American ◽  
Unified Modeling Language ◽  
Problem Instance ◽  
Object Constraint Language ◽  
American University ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Academic Assignments ◽  
Course Timetabling Problem

Timetabling problem is a complex task that is performed by a number of institutions worldwide, which has been usually addressed as an optimization problem where every approach considers the particular constraints of each institution under consideration. In this paper, we describe, model, and propose a solution to the timetabling problem at the División Académica de Ciencias y Tecnologías de la Información of the Universidad Juárez Autónoma de Tabasco (UJAT), México. We modeled the specific constraints of this problem instance using the Object Constraint Language (OCL) of the Unified Modeling Language (UML), and we validated the model while using the state-of-the-art tool USE: UML-based Specification Environment. The solution strategy tackles the problem in two stages: (1) ACA: academic assignments, i.e., assign lectures to professors and (2) TTP: the timetabling process. We developed a Tabu Search customization named Tabu Search with Probabilistic Aspiration Criterion (TS-PAC) in order to solve the timetabling problem, and we developed a software prototype to test our proposal. Two feasible timetables for two different semesters were obtained according to the modeled constraints.

AN ADAPTED CUCKOO OPTIMIZATION ALGORITHM AND GENETIC ALGORITHM APPROACH TO THE UNIVERSITY COURSE TIMETABLING PROBLEM

2014 ◽  
Vol 13 (01) ◽  
pp. 1450002 ◽  
Author(s):  
CHONG KEAT TEOH ◽  
ANTONI WIBOWO ◽  
MOHD. SALIHIN NGADIMAN
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Problem Instance ◽  
Cuckoo Optimization Algorithm ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University

The university course timetabling problem is an NP-hard and NP-complete problem concerned with assigning a specific set of events and resources to timeslots under a highly-constrained search space. This paper presents a novel metaheuristic algorithm entitled adapted cuckoo optimization algorithm which is derived from the cuckoo optimization algorithm and cuckoo search algorithm. This algorithm includes features such as local random walk on discrete data which mimics the behavior of Lévy flights and an Elitism-based mechanism which echoes back the best candidate solutions and prevents the algorithm from plunging into a curse of dimensionality. The algorithm was tested on a problem instance gathered from a University in Malaysia and the results indicate that the proposed algorithm exhibits very promising results in terms of solution quality and computational speed when compared to genetic algorithms.

scholarly journals A solution to the university course timetabling problem using a hybrid method based on genetic algorithms

DYNA ◽  
2020 ◽  
Vol 87 (215) ◽  
pp. 47-56
Author(s):  
Javier Arias-Osorio ◽  
Andrés Mora-Esquivel
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Academic Program ◽  
Mixed Integer ◽  
Computational Time ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course

In this study, we address the current issues that usually manifest during the programming of university courses, classified as University Course Timetabling Problem, which is considered as a NP-hard problem due to the high computational demand that it requires.To solve the problem, a Mixed Integer Linear Programming model is proposed, which serves as a reference when dimensioning the problem and the restrictions that must be considered. Next, a hybrid metaheuristic method is designed based on the HGATS algorithm, Hybrid Genetic Algorithm Tabu Search Approach, developed by [16], which combines the diversification capacity of the Genetic Algorithm with the strategy of intensification of the Tabu Search Algorithm. Finally, the validation of the proposed algorithm is performed using the data from the programming of the classes from the academic periods 2018-1 and 2018-2 for the academic program of Industrial Engineering at the Industrial University of Santander, obtaining interesting solutions in a reasonable computational time, being that the process of organizing the schedule by the coordinator can last from hours to days, depending on your ability.  

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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