非等量单批处理机总完成时间最小化的混合蚁群优化

Rui Xu, Hua-ping Chen, Jun-Hong Zhu, Hao Shao
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引用次数: 0

摘要

本文的目标是最小化具有不同作业大小的单个批处理机器的总完成时间。对于这个问题,每个作业都有相应的处理时间和大小。机器可以批量处理作业,只要批处理作业的总大小不超过机器的容量。一个批的处理时间等于该批中所有作业中最长的处理时间。为此,提出了一种基于批处理序列(bcco)的混沌蚁群优化算法。用随机实例验证了所提方法的有效性。计算结果表明,bacco显著优于文献中提到的其他算法。
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A Hybrid Ant Colony Optimization to Minimize the Total Completion Time on a Single Batch Processing Machine with Non-identical Job Sizes
This paper aims at minimizing the total completion time for a single batch processing machine with non-identical job sizes. For this problem, each job has a corresponding processing time and size. The machine can process the jobs in batches as long as the total size of all the jobs in a batch does not exceed the machine capacity. The processing time of a batch is equal to the longest processing time among all the jobs in that batch. This problem is NP-hard and hence a chaotic ant colony optimization algorithm based on batch sequence (BCACO) is proposed. Random instances were used to test the effectiveness of the proposed approach. Computational results show that BCACO significantly outperforms other algorithms addressed in the literature.
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