Seru Production and its Applications to Smart Manufacturing

根据我们的研究计划，我们通过改变不同的输入元素，成功地生成了一系列与血清形成和加载相关的问题。这些问题集用于分析计算复杂性，确定每个问题是np困难还是多项式，并确定其凸性。正如预期的那样，大多数seru问题是NP-hard和非凸的。对于np困难问题，我们找到了可实现的近似比，并随后开发了多项式算法来获得最优近似比的解。对于非凸问题，我们设计了收敛到局部最优结果的算法。在构建数学模型的过程中，需要识别与智能制造相关的关键要素。这些元素来源于各种来源，例如日本工业工程学会年会等工业会议，研究日语和英语期刊和杂志上的工业案例，以及与有直接工厂经验的MBA学生进行讨论。总之，过去一年开展的工作在分析和模拟血清形成和加载问题方面取得了重大进展。我们采用了一种系统的方法，利用大量的输入变量来生成详细的数学模型。我们继续致力于对这一主题的持续探索，我们期待着来年将带来的突破。

In accordance with our research proposal, we have successfully generated an array of problems related to seru formation and loading by varying different input elements. These problem sets were used to analyze computational complexities, identifying whether each problem was NP-hard or polynomial and determining its convexity. As anticipated, the majority of the seru problems were NP-hard and non-convex. For the NP-hard problems, we found achievable approximation ratios and subsequently developed polynomial algorithms to obtain solutions with optimal approximation ratios. For non-convex problems, we designed algorithms that can converge to local optimal results.The process of constructing mathematical models necessitated the identification of key elements relevant to smart manufacturing. These elements were derived from various sources, such as industrial conferences like the Annual Conference of Japan Institute of Industrial Engineering, studying industrial cases in Japanese and English journals and magazines, and engaging in discussions with our MBA students who have direct factory experience.In conclusion, the work undertaken over the past year represents significant strides in analyzing and modeling seru formation and loading problems. We've adopted a systematic approach that leverages a broad array of input variables to generate detailed mathematical models. We remain committed to the ongoing exploration of this subject, and we look forward to the breakthroughs that the coming year will bring.