Genetic Algorithms for the Assembly Line Balancing Problem: A Real-World Automotive Application
This paper reports the use of Genetic Algorithms (Gas) to solve the assembly line balancing problem in a real-world application: a car assembly facility. The problem is modeled and a standard GA is applied. The line layout solution found by GA reduces by 28.5% the total assembly time of the current line layout, which implies in a significant reduction of costs. This result suggests that the use of GAs in real-world industrial problems can be very promising.
KeywordsLime Production Line Paral
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- 2.Chan, W.T. & Hu, H. (2000). Precast production scheduling with genetic algorithms. Proc. of 2000 Congress on Evolutionary Computation, vol. 2, pp. 1087–1094.Google Scholar
- 3.Goldberg, D.E. (1989).Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.Google Scholar
- 4.Goodman, E.D. (1996). An Introduction to Galopps — The Genetic ALgorithm Optimized for Portability and Parallelism System.Technical Report #96–07–01, Michigan StateUniversity, East Lansing.Google Scholar
- 6.Kopfer, H. (1996).Evolutionary search and the job shop: investigations on genetic algorithms for production scheduling. Heidelberg: Physica-Verlag.Google Scholar
- 9.Nakano, R. (1991). Conventional genetic algorithm for job shop problems.Proc. of the 4th International Conference on Genetic Algorithms, pp. 474–479.Google Scholar