Abstract
Examination timetabling is a hard challenging problem that has posed serious challenges to researchers since the 1960s. Decisions in timetabling seek to ensure that there are no clashes in the timetable and to satisfy other soft constraints as far as possible. In most cases, a variety of conflicting constraints and goals may need to be satisfied, some of which are usually imprecise. Metaheuristic methods and other domain-specific heuristics are often used to address the problem. This chapter presents a fuzzy multi-criterion grouping genetic algorithm to model the timetabling problem. Unique grouping genetic operators are used to take advantage of the group structure of the problem. Furthermore, multifactor evaluation is used to model goals and constraints as weighted normalized cost functions. Fuzzy logic concepts are used to judiciously control the rate of exploration and exploitation of the global optimization process of the algorithm. Thus, fuzzy decision maker’s intuition and expert knowledge are modeled using fuzz theoretic techniques. Experimental analysis based on benchmark problems shows that the approach is competitive. The proposed fuzzy multi-criterion approach contributes to the body of knowledge in the operations research, artificial intelligence, and expert systems.
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Mutingi, M., Mbohwa, C. (2017). Multi-Criterion Examination Timetabling: A Fuzzy Grouping Genetic Algorithm Approach. In: Grouping Genetic Algorithms. Studies in Computational Intelligence, vol 666. Springer, Cham. https://doi.org/10.1007/978-3-319-44394-2_9
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