Metaheuristic with Cooperative Processes for the University Course Timetabling Problem

Abstract

This work presents a metaheuristic with distributed processing that finds solutions for an optimization model of the university course timetabling problem, where collective communication and point-to-point communication are applied, which are used to generate cooperation between processes. The metaheuristic performs the optimization process with simulated annealing within each solution that each process works. The highlight of this work is presented in the algorithmic design for optimizing the problem by applying cooperative processes. In each iteration of the proposed heuristics, collective communication allows the master process to identify the process with the best solution and point-to-point communication allows the best solution to be sent to the master process so that it can be distributed to all the processes in progress in order to direct the search toward a space of solutions which is close to the best solution found at the time. This search is performed by applying simulated annealing. On the other hand, the mathematical representation of an optimization model present in the literature of the university course timing problem is performed. The results obtained in this work show that the proposed metaheuristics improves the results of other metaheuristics for all test instances. Statistical analysis shows that the proposed metaheuristic presents a different behavior from the other metaheuristics with which it is compared.

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).…”

A Novel Optimization Approach for Educational Class Scheduling with considering the Students and Teachers’ Preferences

“…In other words, a professor can teach multiple courses, while each course is listed in professor schools. In addition, in colleges, the class assigned to each subject is unknown in advance, while in schools, this is not the case [3].…”

“…Researchers all around the world have assessed the optimization of curriculum planning and course scheduling in different universities. Cruz-Rosales et al [3] created a two-stage model in which they first considered a feasible solution using novel approaches such as local search and taboo search and then soft constraints using the simulated annealing process. By simulated annealing (SA) algorithm and graph coloring, they obtained a solvable solution.…”

“…By simulated annealing (SA) algorithm and graph coloring, they obtained a solvable solution. Next, the SA algorithm then eliminates soft constraints, as well as rearranges time intervals and moves courses between time intervals [3].…”