An Investigation of Variable Neighbourhood Search for University Course Timetabling

The university course timetabling problem consists, in essence, of assigning lectures to a specific timeslot and room. The goal is to satisfy as many soft constraints as possible while constructing a feasible schedule. In this paper, we present a variable neighbourhood search approach with an exponential monte carlo acceptance criteria. This heuristic search approach is based on random-descent local search. The solution returned after exploring a neighbourhood structure is accepted based on the exponential monte carlo criteria. The approach is tested over eleven established datasets. The results demonstrate that the variable neighbourhood search approach is able to produce solutions that are competitive with state-of-the-art techniques from the literature.