具有不相关并行机器和机器相关工艺阶段的柔性流水车间中的调度:Makespan和生产成本之间的权衡

Ali Hasani , Seyed Mohammad Hassan Hosseini , Shib Sankar Sana
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引用次数: 2

摘要

本文旨在解决包含不相关并行机的柔性流水车间中的双目标调度问题。由于并联机床的技术水平不同,其加工速度和生产成本也各不相同。因此,最小化最大完工时间(Makespan)和总生产成本被认为是两个目标函数。此外,设置时间被认为是序列相关的,该系统考虑机器相关的过程步骤,订单的过程步骤取决于第一阶段中分配的机器。首先,将问题描述为一个双目标数学模型,并将其公式化。由于该问题是强NP难问题,因此引入了一种基于非支配排序遗传算法(NSGA-II)的近似求解方法,为决策者提供了合适的解。通过求解不同的测试问题,与另一种强大的多目标算法(SPEA2)相比,研究了所提出的求解方法的性能。使用误差比(ER)和生成距离(GD)等各种度量的计算结果表明了所提出方法在最优性方面的有效性。其他指标,如间距(S)、多样性(D)和平均理想距离(MID),强调了与竞争对手算法相比,该算法在解决中大型实例方面的优势。此外,补充分析为管理者提供了两个目标之间的适当权衡,以根据他们的偏好选择最佳解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scheduling in a flexible flow shop with unrelated parallel machines and machine-dependent process stages: Trade-off between Makespan and production costs

This paper aims to tackle bi-objective scheduling problem in a flexible flow shop containing unrelated parallel machines in the first stage. Due to the different technology levels of the parallel machines, their process speeds and production costs vary to each other. Therefore, minimizing the maximum completion time (Makespan) and the total production cost are considered as two objective functions. In addition, setup times are considered sequence-dependent and this system considers machine-dependent process steps and the process steps of an order depend on the assigned machine in the first stage. First, the problem is described and formulated as a bi-objective mathematical model. Since the problem is known to be strongly NP-hard, an approximate solution method is introduced based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) to provide proper solutions for decision makers. The performance of the proposed solution method is investigated in comparison to another powerful multi-objective algorithm (SPEA 2) by solving different test problems. The computational results using various metrics such as Error Ratio (ER) and Generational Distance (GD) show the effectiveness of the proposed method in terms of optimality. The other indices such as Spacing (S), Diversification (D), and Mean Ideal Distance (MID) emphasize the superiority of the proposed algorithm compared to the rival algorithm in solving medium and large instances. In addition, supplementary analysis provided proper trade-off between two objectives for managers to select the best solution based on their preferences.

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