Applying Hybrid Fuzzy Multi-Criteria Decision-Making Approach to Find the Best Ranking for the Soft Constraint Weights of Lecturers in UCTP

University course timetabling problem is an NP-hard problem faced periodically by every university of the world which is a time-consuming task. Here, the major goal is to analyze data in order to determine the lecturers’ preferences and constraints and obtain an appropriate ranking to increase their satisfaction by improving it based on soft constraints weights. The proposed method applies a three-step algorithm where in step 1 a fuzzy decision-making approach (fuzzy multi-criteria comparison) is used to prioritize the lecturers; in step 2, a local search algorithm with seven neighborhood structures is employed to improve the ranks by satisfying hard constraints; and in step 3, the genetic algorithm is applied to obtain a proper pattern for adjusting the values of each lecturer’s fitness function. In the proposed algorithm, a list of selective priorities is determined, prioritized and ranked by applying a fuzzy multi-criteria decision-making method based on fuzzy comparison of daily timeslots; then a time table is considered by the combination of local search and genetic algorithms to improve the quality of fitness functions. The proposed method is evaluated by fuzzy multi-criteria decision-making and hybrid algorithms. Here, the dataset of Islamic Azad University, Ahar Branch computer department, is used for simulation. The simulation results show that the proposed method is able to increase the satisfaction of lecturers in terms of their preferences and ranks.