Hybrid Erosion And Deposition Metaheuristic University Teaching Timetabling Framework

Abstract

The research developed a Hybrid Erosion And Deposition Metaheuristic University Teaching Timetabling Algorithm (HEADMUTTA). The hybrid erosion and deposition metaheuristic university teaching timetabling algorithm constructs a university teaching timetable with very few iterations. The Hybrid Erosion And Deposition (HEAD) metaheuristic university teaching timetabling algorithm adopts its behaviour from the HEAD metaheuristic with some concepts adapted from Tabu Search, Simulated Annealing, and Ant Colony metaheuristics. The HEADMUTTA constructs a draft university teaching timetable and further improves it by searching for the best feasible solution that satisfies the predetermined soft and hard rules or constraints. The research also proposed a Hybrid Erosion and Deposition Metaheuristic University Teaching Timetabling framework for implementation. The results indicate that the use of hybrid metaheuristics to solve university teaching timetabling problems improves the quality of the produced university teaching timetables. The HEAD metaheuristic algorithm has a unique feature that allows it to further improve the draft university teaching timetable. Further improvements in the framework may reduce the complexity of NP-hard combinatorial teaching timetabling problems.