babaei, H., hadidi, A. (2017). Generating Optimal Timetabling for Lecturers using Hybrid Fuzzy and Clustering Algorithms. Journal of Artificial Intelligence in Electrical Engineering, 6(21), 9-25.
hamed babaei; amin hadidi. "Generating Optimal Timetabling for Lecturers using Hybrid Fuzzy and Clustering Algorithms". Journal of Artificial Intelligence in Electrical Engineering, 6, 21, 2017, 9-25.
babaei, H., hadidi, A. (2017). 'Generating Optimal Timetabling for Lecturers using Hybrid Fuzzy and Clustering Algorithms', Journal of Artificial Intelligence in Electrical Engineering, 6(21), pp. 9-25.
babaei, H., hadidi, A. Generating Optimal Timetabling for Lecturers using Hybrid Fuzzy and Clustering Algorithms. Journal of Artificial Intelligence in Electrical Engineering, 2017; 6(21): 9-25.
Generating Optimal Timetabling for Lecturers using Hybrid Fuzzy and Clustering Algorithms
1Department of Computer Engineering, Islamic Azad University, Ahar Branch, Ahar, Iran
2Department of Mechanical Engineering, Islamic Azad University, Ahar Branch, Ahar, Iran
Abstract
UCTTP is a NP-hard problem, which must be performed for each semester frequently. The major technique in the presented approach would be analyzing data to resolve uncertainties of lecturers’ preferences and constraints within a department in order to obtain a ranking for each lecturer based on their requirements within a department where it is attempted to increase their satisfaction and develop lecturers timetabling by using clustering algorithms. The first goal of this paper is to improve satisfaction of lecturers and then optimize the ranking of lecturers based on soft constraints weights over their preferences. The proposed method applies a two-step algorithm. At the first step, the department performs timetabling process using fuzzy decision making approach to prioritize and rank lecturers by local search algorithm with seven neighbor structures and genetic algorithm to improve lecturers’ ranks as well as thoroughly satisfying hard constraints over the department in a local manner. In the second step, two clustering and traversing agents are used, where the former clusters lecturers of the department and the latter finds the extra resources. Following the clustering and traversing, in order to reach the major goals of the paper, mapping action is performed based on lecturers’ constraints in resources. In this method, the list of each lecturer’s selective preferences is resolved, prioritized and ranked by applying fuzzy decision making method based on fuzzy comparison of daily and weekly timeslots of per lecturer and then the timetable including department lecturers with their fitness functions is given to the hybrid algorithm in order to improve the quality of fitness function of lecturers within each timetable so that the clustering and mapping is performed based on a desired logic of each lecturer’s fitness function.
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