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

Abstract

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.

Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

“…e genetic crossover and variation operators themselves are adaptive, and the individuals in the population evolve iteratively through the genetic operations, with changes in their crossover and variation rate values determined by the degree of population dispersion or concentration, as well as the size of the population [2]. e amount of population size increases as the crossover rate value becomes larger, but it also leads to an increase in the chance that the more outstanding individuals in the population are destroyed; the size of the variation rate value directly affects the number of newborn populations in the population, and the larger the value of the variation probability, the larger the value of the size of the newborn population, and the greater the possibility of the algorithm jumping out of local convergence to obtain the optimal solution [3]. e complexity of arranging classes increases exponentially with the increase in the school's teaching scale, which is not in direct proportion to the school's teaching scale.…”

The optimal course bidding strategy under limited resource constraint using genetic algorithm

“…The education industry implements the genetic algorithm into optimal school resources such as course scheduling problem, reducing energy consumption and electricity costs [9], creating course schedule with limited factors for the flexibility of classrooms and time arrangements [10][11][12].…”

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

“…In recent years, other strategies have been used that have allowed good quality solutions to be achieved in less time to large problems; however, these do not guarantee the global optimum of the problem. The use of local search methods stands out (Demirović & Musliu, 2017;Goh, Kendall & Sabar, 2017;Rezaeipanah, Matoori & Ahmadi, 2021;Saviniec, Santos & Costa, 2017Song, Liu, Tang, Peng & Chen, 2018), metaheuristic tabu search techniques (Goh et al, 2017;Lü & Hao, 2010;Saviniec et al, 2018) and genetic algorithms (Arias-Osorio & Mora-Esquivel, 2020;Beligiannis, Moschopoulos & Likothanassis, 2009;Feng, Lee & Moon, 2017;Junn, Obit & Alfred, 2018;Khonggamnerd & Innet, 2009;Lin, Chin, Tsui & Wong, 2016;Niknamian, 2021;Raghavjee & Pillay, 2010;Rezaeipanah et al, 2021;Yigit, 2007), solutions based on minimal disturbance (Barták, Müller & Rudová, 2003;Lindahl, Stidsen & Sørensen, 2019;Phillips, Walker, Ehrgott & Ryan 2017), in addition to hyper-heuristics (Ahmed, Özcan & Kheiri, 2015;Junn, Obit, Alfred & Bolongkikit, 2019;Kheiri, Özcan & Parkes, 2016) among others (Cruz-Rosales et al, 2022;Esmaeilbeigi, Mak-Hau, Yearwood, & Nguyen, 2022;Mirghaderi, Alimohammadlo & Fotovvati, 2023;Wouda, Aslan & Vis, 2023).…”