Abstract
The application of genetic algorithms to sequence scheduling problems grew out of attempts to use this method to solve Traveling Salesman Problems. A genetic recombination operator for the Traveling Salesman Problem which preserves adjacency (or edges between cities) was developed; this operator proved to be superior to previous genetic operators for this problem [15]. Recently, a new enhancement to the edge recombination operator has been developed which further improves performance when compared to the original operator. Using this operator in the context of the GENITOR algorithm we obtain best known solutions for 30 and 105 city problems with considerable consistency. Our first test of this approach to scheduling was optimization of a printed circuit production line at Hewlett Packard[16). Success with this problem led us to apply similar methods to production scheduling on a sequencing problem posed by the Coors Brewing Co. This work has resulted in new findings regarding sequencing operators and their emphasis on adjacency, order, and position.
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© 1992 Springer-Verlag Berlin · Heidelberg
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Starkweather, T., Whitley, D., Mathias, K., McDaniel, S. (1992). Sequence Scheduling With Genetic Algorithms. In: Fandel, G., Gulledge, T., Jones, A. (eds) New Directions for Operations Research in Manufacturing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77537-6_8
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DOI: https://doi.org/10.1007/978-3-642-77537-6_8
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