Automated Course Timetabling Optimization Using Tabu-Variable Neighborhood Search Based Hyper-Heuristic Algorithm

Variable Neighborhood Search Based Algorithm for University Course Timetabling Problem

In this paper a variable neighborhood search approach as a method for solving combinatory optimization problems is presented. A variable neighborhood search based algorithm for solving the problem concerning the university course timetable design has been developed. This algorithm is used to solve the real problem regarding the university course timetable design. It is compared with other algor...

Tabu Search Based Hyper-heuristic Approaches to Examination Timetabling

The effect of neighborhood structures on tabu search algorithm in solving university course timetabling problem

The design of course timetables for academic institutions is a very difficult job due to the huge number of possible feasible timetables with respect to the problem size. This process contains lots of constraints that must be taken into account and a large search space to be explored, even if the size of the problem input is not significantly large. Different heuristic approaches have been prop...

An Investigation of a Tabu Search Based Hyper-heuristic for Examination Timetabling

This paper investigates a tabu search based hyper-heuristic for solving examination timetabling problems. The hyper-heuristic framework uses a tabu list to monitor the performance of a collection of low-level heuristics and then make tabu heuristics that have been applied too many times, thus allowing other heuristics to be applied. Experiments carried out on examination timetabling datasets fr...

Adaptive Tabu Search for course timetabling

This paper presents an Adaptive Tabu Search algorithm (denoted by ATS) for solving a problem of curriculum-based course timetabling. The proposed algorithm follows a general framework composed of three phases: initialization, intensification and diversification. The initialization phase constructs a feasible initial timetable using a fast greedy heuristic. Then an adaptively combined intensific...