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Application of improved genetic algorithm for solving machine scheduling

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4 Author(s)
Xiaoling Huang ; Coll. of Transp. Manage., Dalian Maritime Univ., Dalian, China ; Xiuquan Chen ; Hongxing Zheng ; Jinxue Xu

Due to its computational complexity, it is hard to obtain the optimal solution by classical methods, while solving scheduling problems with setup time, so we can only obtain suboptimal solutions by simplified means, which leads to low precision. Aiming at this shortcoming, using classical traveling salesman problem to the scheduling problem was proposed in this paper, and the improved genetic algorithm that based on preferentially proportional selection operator, real number two-point crossover operator and mode mutation operator were used to solve. The simulations results show that this algorithm both solves larger scale scheduling and improves the precision of makespan while meeting minimize makespan.

Published in:

Natural Computation (ICNC), 2010 Sixth International Conference on  (Volume:7 )

Date of Conference:

10-12 Aug. 2010