Abstract
The job shop scheduling problem is a well-known NP hard problem, on which genetic algorithm is widely used. However, due to the lack of the major evolution direction, the effectiveness of the regular genetic algorithm is restricted. In this paper, we propose a new hybrid genetic algorithm to solve the job shop scheduling problem. The particle swarm optimization algorithm is introduced to get the initial population, and evolutionary genetic operations are proposed. We validate the new method on seven benchmark datasets, and the comparisons with some existing methods verify its effectiveness.
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© 2010 Springer-Verlag Berlin Heidelberg
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Tang, J., Zhang, G., Lin, B., Zhang, B. (2010). A Hybrid PSO/GA Algorithm for Job Shop Scheduling Problem. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_69
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DOI: https://doi.org/10.1007/978-3-642-13495-1_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13494-4
Online ISBN: 978-3-642-13495-1
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