Flow-Shop Scheduling Problem Based Improved Adaptive Genetic Algorithms

Acta Scientiarum Naturalium Universitatis Pekinensis

Flow-Shop Scheduling Problem Based Improved Adaptive Genetic Algorithms

CHI Bin¹, YE Qingkai¹, XING Fei²

¹Department of Mechanics and Engineering Science, Peking University, Beijing, 100871, E-mail: chibin@mech.pku.edu.cn; ²Department of Mathematics, Inner Mongolia University, Hohhot, 010021

Received:2002-05-14 Online:2003-05-20 Published:2003-05-20

用改进的遗传算法求解流水车间作业排序问题

迟彬¹,叶庆凯¹,行飞²

¹北京大学力学与工程科学系，北京，100871；²内蒙古大学数学系，呼和浩特，010021

Abstract

Abstract: Two kinds of improved adaptive genetic algorithms and encoding & decoding methods are presented. The experiment data shows that the improved adaptive genetic algorithms are superior to the pure genetic algorithms and other adaptive genetic algorithms in qualify of solution and efficiency.

Key words: flow-shop, scheduling, adaptive, genetic-algorithms

摘要： 针对流水车间(Flow-shop)作业排序问题，提出了两种改进的自适应遗传算法并给出了两种编码、解码方案。把此算法与现有的几种解法进行了比较，实验数据表明，改进的遗传算法在求解质量和效率上均优于传统的遗传算法和其他自适应遗传算法。

关键词: 流水车间, 作业排序, 自适应, 遗传算法

CLC Number:

TP18

CHI Bin,YE Qingkai,XING Fei. Flow-Shop Scheduling Problem Based Improved Adaptive Genetic Algorithms[J]. Acta Scientiarum Naturalium Universitatis Pekinensis.

迟彬,叶庆凯,行飞. 用改进的遗传算法求解流水车间作业排序问题[J]. 北京大学学报（自然科学版）.

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URL: https://xbna.pku.edu.cn/EN/

https://xbna.pku.edu.cn/EN/Y2003/V39/I3/294

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