IEEE Trans Cybern. 2020 Jun;50(6):2425-2439. doi: 10.1109/TCYB.2019.2943606. Epub 2019 Oct 8.
In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.
在本文中，我们提出了一种混合人工蜂群（ABC）算法来解决具有恶化作业的并行批处理分布式流水作业问题（DFSP）。在所考虑的问题中，有两个阶段如下：1）在第一阶段，研究了一个 DFSP；2）在第一阶段完成后，每个作业将在第二阶段转移和组装，其中研究了并行批处理约束。在两个阶段中，都考虑了恶化作业约束。在所提出的算法中，首先提出了两种特定于问题的启发式方法，即批分配启发式和右移启发式，这可以显著减少完工时间。接下来，根据问题的约束和目标开发了编码和解码方法。为分布式流水作业和并行批处理阶段设计了五种类型的局部搜索算子。此外，研究了一种新的侦察蜂启发式方法，该方法考虑了全局和局部最优解收集的有用信息，从而可以提高搜索性能。最后，基于几个著名的基准和实际的工业实例，并通过全面的计算比较和统计分析，与几种算法相比，所提出的算法在解决方案质量和种群多样性方面都表现出了优异的性能。
