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Dynamic Scheduling Strategy of RGV

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DOI: 10.23977/amce.2019.012


Wen Fu, Qingchang Wang, Yunzhao Li, Heng Zhang, Fei Wang and Huan Li

Corresponding Author

Wen Fu


This paper focuses on the dynamic scheduling strategy of intelligent RGV (Rail Guided Vehicle). The goal of this problem is to find an optimal scheduling scheme for processing some materials within a certain time (one shift). To make the problem easier to solve, we convert the goal of the problem into a scheduling scheme that solves the processing of n-piece materials and requires the shortest time. At the same time, according to the purpose of this problem, the shortest time is limited to one shift, and then the number of materials is slightly adjusted to make the time close to eight hours. In the process, we establish a mathematical model and adopt a rule-based adaptive genetic algorithm. To avoid obtaining a low-reasonability scheme and reduce the efficiency of the algorithm, we propose some rules for manual intervention when generating the initial population. We use the adaptive crossover and mutation probability and flexible population to replace the fixed parameters in the traditional genetic algorithm. Finally, we analyze the performance of the algorithm by MATLAB simulation experiment and the effectiveness of the algorithm is verified.


Rule-based Adaptive Genetic Algorithm, Adaptive Crossover and Mutation Probability, Flexible Population

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