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

使用聚类和统计分析设计产品多样性的制造系统

• 批准号: RGPIN-2017-04858
• 负责人: Li, Simon
• 金额: $ 1.53万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711267
• 关键词: Design; manufacturing; systems; product; variety

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.

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。