Collaborative Research: Randomized Distributed Data Structures for Product Design

这个合作项目的目标是使产品回收和再利用在经济和环境上可行。这对工程设计很重要，因为尽管组件重用和再制造可以潜在地降低成本、减少环境影响并提高客户满意度，但计算问题将设计分析限制在一组静态的、行业平均的模型输入上。现实世界的实现需要来自各种广泛分布的数据源的动态输入。需要完成的任务包括：开发一个系统，利用广泛分布的数据来源，就部件回收、再利用、再制造或处置作出决策；严格规范具有概率行为的共享对象；开发高效、容错的分布式算法实现共享对象开发一个设计系统，通过在整个产品生命周期中利用来自多个来源的反馈来提高客户满意度。更广泛的影响包括回收目前在废弃产品中浪费的经济价值，降低消费者成本和对环境的影响。同时，我们的计算基础设施的可扩展性也将得到扩展。这个项目将被整合到一个工业赞助的高级设计课程中，因此技术向学生和行业的转移将是广泛和及时的。为两个不同系（一般工程和计算机科学）的本科和研究生课程开发新的跨学科课程材料，将加强教育基础设施。未被充分代表的群体将从PI的研究报告中受益，其社会相关性被认为可以激励女性和少数民族学生从事工程研究。

The objective of this collaborative project is to make product take-back and reuse economically and environmentally viable. It is important for engineering design because although component reuse and remanufacture can potentially decrease cost, decrease environmental impact, and increase customer satisfaction, computational issues have limited design analysis to a single set of static, industry-average model inputs. Real world implementation requires dynamic input from a variety of widely distributed data sources. Tasks to be completed include: development of a system for making decisions regarding component recycle, reuse, remanufacturing or disposal that utilizes data from widely distributed sources; rigorous specification of shared objects that have probabilistic behavior; development of efficient and fault-tolerant distributed algorithms to implement the shared objects; development of a design system that increases customer satisfaction by utilizing feedback from multiple sources throughout the product lifecycle.Broader impacts include recovering the economic value that is currently wasted in discarded products, decreasing both consumer costs and environmental impacts. Also, the scalability of our computing infrastructure will be expanded. This project will be integrated into an industrially sponsored senior design course, so technology transfer to students and industry will be broad and timely. The educational infrastructure will be enhanced by development of new interdisciplinary course materials for both undergraduate and graduate courses in two different departments (General Engineering and Computer Science). Underrepresented groups will benefit by the PI's presentations of this research, whose social relevance is known to motivate women and minority students to pursue engineering studies.