Managing the tabu list length using a fuzzy inference system: an application to examination timetabling We present an application of tabu search (TS) to the examination timetabling problem. One of the drawbacks of this meta-heuristic is related to the need of tuning some parameter (like tabu tenure) whose value affects the performance of the algorithm. The importance of developing an automatic procedure is clear considering that most of the users of timetabling software, like academic staff, do not have the expertise to conduct such tuning. The goal of this paper is to present a method to automatically manage the memory in the TS using a decision expert system. More precisely a fuzzy inference rule based system (FIRBS) is implemented to handle the tabu tenure based on two concepts, “frequency” and “inactivity”. These concepts are related respectively with the number of times a move is introduced in the tabu list and the last time (in number of iterations) the move was attempted and prevented by the tabu status. Computational results show that the implemented FIRBS handles well the tuning of the tabu status duration improving, as well, the performance of tabu search.