Design of manufacturing systems for product variety using cluster and statistical analysis

该研究计划的范围是优化制造系统，以支持产品的多样性和可持续性。为了在市场上保持竞争力，原始制造商（OM）需要为客户提供更多的产品选择，并满足环境法规和社会期望。在这种背景下，虽然形式优化模型在系统设计中得到了广泛的应用，但它们往往面临着模型和计算复杂性的挑战（例如，大量的整数变量和收敛问题）。本研究将研究和发展制造系统设计的聚类和统计技术，而不是采用优化方法。新颖之处在于开发一种自下而上的方法来系统地解决复杂的结构决策，其目标是在解决收敛性和计算时间上取得更好的性能，以及与元启发式算法相当的最优性能。拟议的研究计划分为两个主题。第一个主题是元胞制造系统（CMS）聚类算法的发展。研究目标之一是提高层次聚类算法在CMS设计中搜索最优结构决策的能力。方法方法包括耦合分布和可逆HC的统计分析，以避免过早群体的锁定。此外，我们还将关注健壮性和模块化问题。在鲁棒性方面，采用聚类方法将相关的更改进行连接，并有目的地部署资源，以减少不必要的传播影响。在模块化方面，研究思路是利用聚类概念将产品架构的模块化与制造系统的模块化相匹配，增强系统适应市场变化的灵活性。第二个主题是可持续性闭环制造系统的设计。总体目标是支持OM参与产品的生命周期结束管理。考虑三个方面：组装，拆卸和重新组装。“装配”指的是正向生产，“拆卸”和“重组”指的是考虑寿命终止选项和产品回收的逆向循环。在此应用程序中，集群方法用于配置该网络系统以改进可持续性措施。***从研究到实践，我们将使开发的方法和知识以实用的专有技术的形式为加拿大制造业提供（例如，如何最大限度地减少变更传播和设计健壮的系统）。对于零部件供应商来说，他们可以学习如何提高他们的制造灵活性以获得更多的合同机会。对于OM来说，他们可以通过设计一个闭环网络来管理材料首次使用后的流动，从而促进可持续性。

The scope of this research program is the optimization of manufacturing systems to support product variety and sustainability. To stay competitive in the market, original manufacturers (OM) need to satisfy customers with more product options and meet the environmental regulations and societal expectations. In this context, while formal optimization models have been widely used in system design, they often face the challenges of model and computational complexity (e.g., high number of integer variables and convergence issues). Instead of employing optimization methods, this research will investigate and develop clustering and statistical techniques for the design of manufacturing systems. The novelty is to develop a bottom-up approach to address complex structural decisions systematically with the target of better performance on solution convergence and computing time, as well as comparable optimality performance with metaheuristic algorithms.***The proposed research program is organized into two themes. The first theme is the development of clustering algorithms for cellular manufacturing systems (CMS). One research goal is to improve the capability of hierarchical clustering (HC) to search for optimal structural decisions in the design of CMS. The methodological approach includes statistical analysis of coupling distributions and reversible HC to avoid the lock-in of premature groups. Also, we will focus on the issues of robustness and modularity. Regarding robustness, the clustering approach is applied to concatenate relevant changes and deploy resources purposely to reduce unnecessary propagation effects. Regarding modularity, the research idea is to match the modularity of product architecture and manufacturing systems using clustering concepts to enhance system flexibility for market changes.***The second theme is the design of a closed-loop manufacturing system for sustainability. The general goal is to support OM to participate in the product's end-of-life management. Three aspects are considered: assembly, disassembly, and reassembly. While “assembly” is referred to the forward production, “disassembly” and “reassembly” are referred to the backward loop that considers the end-of-life options and product recovery. In this application, the clustering approach is used to configure this network system to improve sustainability measures.***From research to practice, we will make the developed methodology and knowledge accessible to the Canadian manufacturing sector in a form of practical know-how (e.g., how to minimize change propagation and design robust systems). For component suppliers, they can learn how to enhance their manufacturing flexibility for more contract opportunities. For OM, they can promote sustainability by designing a close-loop network that manages the flow of materials after their first usage.