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Grouping Genetic Algorithms pp 161–182Cite as

Multi-Criterion Examination Timetabling: A Fuzzy Grouping Genetic Algorithm Approach

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 666))

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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Authors and Affiliations

  1. Faculty of Engineering, Namibia University of Science and Technology, Windhoek, Namibia

    Michael Mutingi

  2. Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Michael Mutingi & Charles Mbohwa

Authors
  1. Michael Mutingi
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  2. Charles Mbohwa
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Correspondence to Michael Mutingi .

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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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  • DOI: https://doi.org/10.1007/978-3-319-44394-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44393-5

  • Online ISBN: 978-3-319-44394-2

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