Genetic algorithms for combinatorial optimization: The assembly line balancing problem

Genetic algorithms for combinatorial optimization: The assembly line balancing problem

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Article ID: iaor19982430
Country: United States
Volume: 6
Issue: 2
Start Page Number: 161
End Page Number: 173
Publication Date: Mar 1994
Journal: ORSA Journal On Computing
Authors: ,
Keywords: optimization
Abstract:

Genetic algorithms are one example of the use of a random element within an algorithm for combinatorial optimization. We consider the application of the genetic algorithm to a particular problem, the Assembly Line Balancing Problem. A general description of genetic algorithms is given, and their specialized use on our test-bed problems is discussed. We carry out extensive computational testing to find appropriate values for the various parameters associated with this genetic algorithm. These experiments underscore the importance of the correct choice of a scaling parameter and mutation rate to ensure the good performance of a genetic algorithm. We also describe a parallel implementation of the genetic algorithm and give some comparisons between the parallel and serial implementations. Both versions of the algorithm are shown to be effective in producing good solutions for problems of this type (with appropriately chosen parameters).

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