智能制造时代具有批量流的非置换流水车间过程数学建模

D. Rossit, F. Tohmé, Rodrigo Introcaso, Jeanette Rodríguez
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引用次数: 0

摘要

工业4.0正在利用工业的生产能力。工业4.0推动的深度数字化使控制技能能够扩展到车间的详尽细节。然后,可以设计和实施新的规划策略。我们提出了表示非排列流程车间流程的数学模型,结合了工业4.0的特点和以客户为中心的关注。基本上,我们研究批量流对随后的优化问题的影响,因为工业4.0技术大大增强了在制品库存控制。因此,可以利用将生产批次细分为更小的子批次的优势,正如批次流所建议的那样。为了验证这一假设,我们使用了一种新的方法来解决非置换流车间问题,该问题需要大量的流策略,并将总延迟作为目标函数。我们的分析表明,随着机器数量的增加,批量流式处理越来越能提高结果。我们还发现,随着子批次的增加,改进不那么陡峭,增加了解决方案的计算成本。这表明微调子批次的最大数量以避免额外成本是高度相关的。
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Mathematical Modelling of Non-Permutation Flow Shop Processes with Lot Streaming in the Smart Manufacturing Era
Industry 4.0 is leveraging the production capabilities of the industry. The deep digitalization that Industry 4.0 promotes enables to extend control skills to an exhaustive detail in the shop floors. Then, new planning strategies can be designed and implemented. We present mathematical models to represent non-permutation flow shop processes, incorporating Industry 4.0 features and customer-focused attention. Basically, we study the impact of lot streaming on the ensuing optimization problems, since the work-in-process inventory control is considerably enhanced by Industry 4.0 technologies. Thus, is possible to take advantage of subdividing the production lots into smaller sublots, as lot streaming proposes. To test this hypothesis we use a novel approach to non-permutation flow shop problems which requires a lot streaming strategy, incorporating total tardiness as objective function. Our analysis indicates that lot streaming improves results increasingly with the number of machines. We also find that the improvement is less steep with more sublots, increasing the computational cost of solutions. This indicates that it is highly relevant to fine tune the maximum number of sublots to avoid extra costs.
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来源期刊
CiteScore
7.90
自引率
0.00%
发文量
25
审稿时长
15 weeks
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