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首页> 外文期刊>International Journal of Production Research >Ant colony optimisation algorithms for two-stage permutation flow shop with batch processing machines and nonidentical job sizes
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Ant colony optimisation algorithms for two-stage permutation flow shop with batch processing machines and nonidentical job sizes

机译：带有批处理机器且工作量不相同的两阶段置换流水车间的蚁群优化算法

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摘要

This paper focuses on minimising the maximum completion time for the two-stage permutation flow shop scheduling problem with batch processing machines and nonidentical job sizes by considering blocking, arbitrary release times, and fixed setup and cleaning times. Two hybrid ant colony optimisation algorithms, one based on job sequencing (JHACO) and the other based on batch sequencing (BHACO), are proposed to solve this problem. First, max-min pheromone restriction rules and a local optimisation rule are embedded into JHACO and BHACO, respectively, to avoid trapping in local optima. Then, an effective lower bound is estimated to evaluate the performances of the different algorithms. Finally, the Taguchi method is adopted to investigate and optimise the parameters for JHACO and BHACO. The performances of the proposed algorithms are compared with that of CPLEX on small-scale instances and those of a hybrid genetic algorithm (HGA) and a hybrid discrete differential evolution (HDDE) algorithm on full-scale instances. The computational results demonstrate that BHACO outperforms JHACO, HDDE and HGA in terms of solution quality. Besides, JHACO strikes a balance between solution quality and run time.
机译：本文着重于通过考虑阻塞，任意释放时间以及固定设置和清洁时间，最大程度地缩短批处理机器和不相同作业大小的两阶段置换流水车间调度问题的最大完成时间。为解决这一问题，提出了两种混合蚁群优化算法，一种基于作业排序（JHACO），另一种基于批处理排序（BHACO）。首先，最大-最小信息素限制规则和局部优化规则分别嵌入到JHACO和BHACO中，以避免陷入局部最优中。然后，估计有效的下限以评估不同算法的性能。最后，采用Taguchi方法研究和优化了JHACO和BHACO的参数。将该算法的性能与CPLEX在小规模实例上的性能进行了比较，并将其与混合遗传算法（HGA）和混合离散差分进化（HDDE）在全尺寸实例上的性能进行了比较。计算结果表明，BHACO在解决方案质量方面优于JHACO，HDDE和HGA。此外，JHACO在解决方案质量和运行时间之间取得平衡。

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