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A Hybrid Discrete Artificial Bee Colony Algorithm for Multi-objective Blocking Lot-Streaming Flow Shop Scheduling Problem

机译:混合离散人工蜂群算法的多目标分批流水作业调度问题

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A blocking lot-streaming flow shop (BLSFS) scheduling problem involves in splitting a job into several sublots and no capacity buffers with blocking between adjacent machines. It is of popularity in real-world applications but hard to be effectively solved in light of many constrains and complexities. Thus, the research on optimization algorithms for the BLSFS scheduling problem is relatively scarce. In view of this, we proposed a hybrid discrete artificial bee colony (HDABC) algorithm to tackle the BLSFS scheduling problem with two commonly used and conflicting criteria, i.e., makespan and earliness time. We first presented three initialization strategies to enhance the quality of the initial population, and then developed two novel crossover operators by taking full of valuable information of non-dominated solutions to enhance the capabilities of HDABC in exploration. We applied the proposed algorithm to 16 instances and compared with three previous algorithms. The experimental results show that the proposed algorithm clearly outperforms these comparative algorithms.
机译:批量流处理车间(BLSFS)的调度问题涉及将作业拆分为几个子批处理,并且没有容量缓冲区,相邻机器之间也存在阻塞。它在现实世界的应用程序中很流行,但鉴于许多限制和复杂性,很难有效解决。因此,针对BLSFS调度问题的优化算法的研究相对较少。有鉴于此,我们提出了一种混合离散人工蜂群(HDABC)算法,以两个常用且相互冲突的标准(即有效期和提前时间)解决BLSFS调度问题。我们首先提出了三种初始化策略来提高初始种群的质量,然后通过充分利用非支配解决方案的宝贵信息来开发两个新颖的交叉算子,以增强HDABC的勘探能力。我们将提出的算法应用于16个实例,并与之前的三个算法进行了比较。实验结果表明,该算法明显优于这些比较算法。

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