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Hybrid Algorithm Based on an Estimation of Distribution Algorithm and Cuckoo Search for the No Idle Permutation Flow Shop Scheduling Problem with the Total Tardiness Criterion Minimization

机译：基于分配算法估计和布谷鸟搜索的混合算法，以总拖延时间准则为最小的无空闲排列流水车间调度问题

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摘要

The no idle permutation flow shop scheduling problem (NIPFSP) is a popular NP-hard combinatorial optimization problem, which exists in several real world production processes. This study proposes a novel hybrid estimation of the distribution algorithm and cuckoo search (CS) algorithm (HEDA_CS) to solve the NIPFSP with the total tardiness criterion minimization. The problem model is built on the basis of the starting and ending time point of each job. A discrete solution representation method is applied in HEDA_CS to increase the operation efficiency. A novel probability matrix build method is also designed within the knowledge of the processing time matrix. The partially-mapped crossover operation works effectively during the CS phase. A suitable knowledge-based local search is also designed in the HEDA_CS to balance the exploitation and exploration. Finally, many simulations based on the new hard Ruiz benchmarks are conducted. Computational results demonstrate the effectiveness of the proposed HEDA_CS.
机译：无空闲排列流水车间调度问题（NIPFSP）是一个流行的NP硬组合优化问题，存在于多个实际生产过程中。这项研究提出了一种新的分布估计和布谷鸟搜索（CS）算法（HEDA_CS）的混合估计，以解决总时延标准最小化的NIPFSP问题。问题模型是基于每个作业的开始和结束时间点构建的。在HEDA_CS中使用离散解决方案表示方法来提高操作效率。在处理时间矩阵的知识范围内，还设计了一种新颖的概率矩阵构建方法。在CS阶段，部分映射的交叉操作有效地工作。 HEDA_CS中还设计了合适的基于知识的本地搜索，以平衡开发和勘探。最后，基于新的硬Ruiz基准进行了许多模拟。计算结果证明了所提出的HEDA_CS的有效性。

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