A hybrid grouping genetic algorithm approach

Michael Mutingi, Charles Mbohwa

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


Assembly line balancing is a highly combinatorial and complex problem that deals with the assignment of individual work elements or tasks to workstations with the objective of minimizing the assembly cost as much as possible. Challenges associated with this problem were discussed, including the group orientation of the problem, the presence of precedence (order dependency) constraints between tasks executed within each group and across groups, and other problem-specific constraints. As a NP-hard computational problem, heuristic algorithms and metaheuristics have been used to solve the assembly line balancing problem. This chapter presented a hybrid grouping genetic algorithm to address complex problems. The algorithm hybridizes basic constructive heuristics, enhanced genetic operators, and other techniques to improve the optimization search process. The performance of the proposed hybrid algorithm was compared to the basic GA based on established test problems. Results of the comparative computational experiments showed that the hybrid algorithm is effective and efficient, in terms of the quality of solutions (measured by the number of workstations and the realized cycle time), as well as the average computation times. The proposed hybrid algorithm can be developed into a decision support system to assist decision makers in making decisions associated with assembly line balancing.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages15
Publication statusPublished - 2017

Publication series

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

ASJC Scopus subject areas

  • Artificial Intelligence


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