Self-Learning Genetic Algorithms for RGV Optimization Scheduling

Wei Hua Xu; Jian Xiong; Guo Zhong Yao

doi:10.4028/www.scientific.net/AMR.562-564.1706

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 562-564Self-Learning Genetic Algorithms for RGV...

Self-Learning Genetic Algorithms for RGV Optimization Scheduling

Abstract:

Through the in-depth analysis on working trait and scheduling policy of straight Rail Guided Vehicle (RGV), a multi-objective genetic algorithm (GA) is proposed for RGV scheduling problems, of which the start-stop, waiting and complex task of the straight RGV was considered. On the base, the real-time scheduling policy of straight RGV is set up based on self-learning and the improved GA. The rule of encoding, selecting, crossover selection and variation probability of the model was studied, and the self-learning expert database and searching the optimal compromising solutions were given to enhance calculation efficiency. In closing, the results of simulation and engineering application show that the system captaincy of RGV is effectively improved, and the model is feasible and effective.

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Periodical:

Advanced Materials Research (Volumes 562-564)

Pages:

1706-1711

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.562-564.1706

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Online since:

August 2012

Authors:

Wei Hua Xu, Jian Xiong, Guo Zhong Yao

Keywords:

Genetic Algorithm (GA), Optimisation Scheduling, Rail Guided Vehicle (RGV)

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