GOALI: Modeling Product Variety Induced Manufacturing Complexity for Assembly System Design

该奖项的目的是开发多阶段混合模型装配系统中产品多样性引起的制造复杂性的数学模型，并深入了解这种复杂性对系统性能的影响。这项研究将由密歇根大学的教师和学生以及通用汽车研发制造系统实验室的研究工程人员合作进行，并由通用汽车汽车装配厂积极参与。 具体而言，将开发多阶段模型，通过考虑在每个站点诱导的选择复杂性和从上游站点转移的复杂性来表征复杂性的传播。将开发贝叶斯网络（BN）模型，以量化复杂性对系统性能的影响，包括质量和操作员的生产力。验证后，开发的复杂性模型将被应用于改进装配系统设计和综合制造系统开发中复杂性管理的指导方针。 产品多样性导致的制造复杂性是当今所有制造商面临的一个重要问题。由于制造商正在开发定制产品以满足个人需求，因此竞争环境日益激烈。因此，所提出的研究将具有广泛的潜在利益，因为所开发的模型、算法和准则将能够帮助制造系统设计者在设计制造系统时管理复杂性，这将导致操作员和系统性能的提高。 学生将在通用汽车公司实习，从而直接协助技术转让。 研究成果也将被开发成模块的研究生课程？面向设计和制造的装配建模通过密歇根大学技术增强学习计划在全球范围内提供。

This objective of this award is to develop mathematical models on product variety induced manufacturing complexity in multi-stage, mixed model assembly systems and gain insights on the impact of such complexity on system performance. This research will be pursued collaboratively by faculty and students at the University of Michigan and research engineering staff at General Motors R & D Manufacturing Systems Laboratory with active participation from GM vehicle assembly plants. Specifically, multi-stage models will be developed to characterize the propagation of complexity by considering the choice complexity induced at each station and the transferred complexity from the up-stream stations. Bayesian network (BN) models will be developed to quantify the impact of the complexity on the system performance, including quality and operator productivity. When validated, the developed complexity models will be applied to improve assembly system design and synthesize guidelines for managing complexity in manufacturing system development. Product variety induced manufacturing complexity is a critical problem faced by all manufacturers in today?s competitive environment as manufacturers are developing customized products to respond to individual needs. As such, the proposed research will have broad potential benefits in that the developed models, algorithms, and guidelines will be able to assist manufacturing system designers in managing complexity when designing manufacturing systems, which will result in improved operator and system performance. Students will serve as interns at GM, thus directly assisting in the transfer of technology. Research results will also be developed into modules for a graduate course in ?Assembly Modeling for Design and Manufacturing?, offered globally through the University of Michigan technology enhanced learning program.