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Minimizing makespan and flowtime in a parallel multi-stage cellular manufacturing company
Robotics and Computer-Integrated Manufacturing ( IF 9.1 ) Pub Date : 2021-05-21 , DOI: 10.1016/j.rcim.2021.102182
İlkay Saraçoğlu , Gürsel A. Süer , Patrick Gannon

This study proposes a 3-phase solution approach for a multi-product parallel multi-stage cellular manufacturing company. The study focuses on a case study involving a shoe manufacturing plant in which products are produced according to their due dates. The investigated manufacturing process has three stages, namely lasting cells, rotary injection molding cells, finishing-packaging cells. System performance is measured based on total flowtime and makespan. We propose a 3-phase solution approach to tackle the problem; 1) the first phase of the proposed approach allocates manpower to operations in the lasting cells and finishing-packaging cells, independently. The objective is to maximize the production rates in these cells. 2) The second phase includes cell loading to determine product families based on a similarity coefficient using mathematical modeling and genetic algorithms (GA). The proposed GA algorithm for cell loading performs mutation prior to crossover, breaking from traditional genetic algorithm flow. The performance measures flow time and makespan are considered in this phase. 3) Flow shop scheduling is then performed to determine the product sequence in each (lasting, rotary injection molding, finishing-packaging) cell group. This 3-phase solution approached is repeated with alternative manpower level allocation to lasting and finishing-packaging cells where the total manpower level remains the same.



中文翻译:

在并行多级蜂窝制造公司中最小化制造时间和生产时间

这项研究为多产品并行多阶段蜂窝制造公司提出了一种三相解决方案方法。这项研究集中在一个案例研究中,该案例涉及一家鞋类制造厂,根据该产品的到期日生产产品。所研究的制造过程分为三个阶段,即持久性单元,旋转注射成型单元,精整包装单元。系统性能是根据总的流动时间和制造时间来衡量的。我们提出了一种三相解决方案来解决该问题。1)所提出方法的第一阶段将人力分别分配给持久单元和最终包装单元中的操作。目的是使这些电池的生产率最大化。2)第二阶段包括使用数学模型和遗传算法(GA)基于相似系数确定单元负荷。提出的用于细胞加载的遗传算法在交叉之前执行变异,这与传统的遗传算法流程不同。在此阶段中,将考虑性能指标的流动时间和延展时间。3)然后执行流水作业调度,以确定每个(持久,旋转注射成型,完成包装)单元组中的产品顺序。重复进行此3阶段解决方案,并在总人力水平保持不变的情况下,对持久包装和最终包装的单元进行替代性的人力分配。突破了传统的遗传算法流程。此阶段将考虑性能指标的流动时间和制造时间。3)然后执行流水作业调度,以确定每个(持久,旋转注射成型,完成包装)单元组中的产品顺序。重复进行此3阶段解决方案,并在总人力水平保持不变的情况下,对持久包装和最终包装的单元进行替代性的人力分配。突破了传统的遗传算法流程。在此阶段中,将考虑性能指标的流动时间和延展时间。3)然后执行流水作业调度,以确定每个(持久,旋转注射成型,完成包装)单元组中的产品顺序。重复进行此3阶段解决方案,并在总人力水平保持不变的情况下,对持久包装和最终包装的单元进行替代性的人力分配。

更新日期:2021-05-22
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