Multi-criterion examination timetabling: A fuzzy grouping genetic algorithm approach

Michael Mutingi, Charles Mbohwa

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The examination timetabling problem is a hard problem that has attracted considerable attention of researchers and practitioners worldwide. Timetabling decision process must ensure that there are no clashes in the timetable and satisfy soft constraints as much as possible. Since the problem is highly complex, decision support systems often incorporate metaheuristic methods and domain-specific heuristics so as to address the problems more efficiently and effectively. In this vein, the chapter presented a fuzzy multi-criterion approach to model the timetabling problem. All constraints are modeled as weighted normalized cost functions using the multifactor evaluation method. The group encoding scheme adopted in this chapter enables the algorithm to capture the group structure of the problem. Enhancing fuzzy logic concepts is used to control the rate of exploration and exploitation during the search and optimization process of the algorithm. The proposed approach contributes to the body of knowledge in the operations research and management science community. First, the suggested approach can model the fuzzy parameters of the problem, such as decision maker's choices, and preferences. Second, the approach uses unique advanced grouping genetic operators to take advantage of the group structure of the problem. Third, the approach provides a more efficient algorithm, in comparison with past approaches.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages22
Publication statusPublished - 2017

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence


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