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Assembly Line Balancing Using Genetic Algorithms with Heuristic‐Generated Initial Populations and Multiple Evaluation Criteria *
Author(s) -
Leu YowYuh,
Matheson Lance A.,
Rees Loren Paul
Publication year - 1994
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1994.tb00822.x
Subject(s) - assembly line , computer science , heuristic , genetic algorithm , algorithm , line (geometry) , mathematical optimization , mathematics , artificial intelligence , machine learning , engineering , mechanical engineering , geometry
We use genetic algorithms (GA) to solve the assembly line balancing (ALB) problem. Inparticular, we show how this technique can be used to generate feasible line balances, improve upon solutions obtained by other heuristics reported in the literature, and utilizeany one or more evaluation criteria that can be expressed in functional form. The procedure is demonstrated with two examples: (1) intimating the improvement of heuristic‐generated ALB solutions by including them in the GA initial population, and (2) the possibility of balancing assembly lines with multiple criteria and side constraints. These examples suggest that GA can be a powerful tool in ALB. To investigate the utility of GA on single‐criterion problems, an experiment is conducted that compares both the GA approach and conventional heuristics. Results indicate that the GA solutions are significantly improved over the heuristic solutions under the conditions studied. It is also found that the presence of heuristic‐generated conventional solutions in the GA initial population leads to statistically preferred results.