Metaheuristic with Cooperative Processes for the University Course Timetabling Problem

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.

School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators’ availabilities, preferences, and expertise levels.

A collaborative approach for solving the university course timetabling problem

This work proposes a collaborative approach for solving the university course timetabling problem (UCTP). A prototype was developed and used for a computer sciencecourse at the Federal Fluminense University in Brazil. The main idea is that students, professors, and course coordinators contribute collaboratively to course timetabling through an app. These contributions employ heuristics, which is responsible for timetabling to improve the solution to the problem. Results and future works are described herein.

Solving a University Course Timetabling Problem Based on AACSB Policies

The purpose of this research is to solve the university course timetabling problem (UCTP) that consists of designing a schedule of the courses to be offered in one academic period based on students’ demand, faculty composition and institutional constraints considering the policies established in the standards of the Association to Advance Collegiate Schools of Business (AACSB) accreditation. These standards involve faculty assignment with high level credentials that have to be fulfilled for business schools on the road to seek recognition and differentiation while providing exceptional learning. A new mathematical model for UCTP is proposed. The model allows the course-section-professor-time slot to be assigned for an academic department strategically using the faculty workload, course overload, and the fulfillment of the AACSB criteria. Further, the courses that will require new hires are classified according to the faculty qualifications stablished by AACSB. A real-world case is described and solved to show the efficiency of the proposed model. An analysis of different strategies derived from institutional policies that impacts the resulting timetabling is also presented. The results show the course overload could be a valuable strategy for helping mitigate the total of new hires needed. The proposed model allows to create the course at the same time the AACSB standards are met.