Collaborative Research: Reducing the Burden of Global Materials Manufacture: Enabling Increased Use of Secondary and Renewable Materials in Production Planning

合作研究：减轻全球材料制造的负担：在生产计划中增加二次和可再生材料的使用

• 批准号: 1133422
• 负责人: Gabrielle Gaustad
• 金额: $ 7.92万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1133422&HistoricalAwards=false
• 关键词: Collaborative; Research; Reducing; Burden; Global

1133286 Kirchain/1133422 GaustadThis research will produce a systematic mapping of relative performance of deterministic and several forms of uncertainty-aware stochastic batch planning models for a range of production contexts. Specific contexts to be explored include at least three forms of batch quality performance functions (linear, power-law, and logarithmic) and four distributional forms of raw material (RM) quality (Lognormal, Max Extrema, Gamma, or Student's t). Additionally, this research will provide both analytical and quantitative case analysis that supports the economic and resource efficiency value of diversification of interdependence within industrial ecosystems. For three planned industrial case analyses, this research will quantify the distributional nature of RM quality and the current and potential ability to utilize secondary and renewable raw materials (SRRMs) while examining the impact of the number of raw materials attributes. The educational component of this research seeks to develop methods and case studies to incorporate sustainability into engineering education. These course materials will be integrated into graduate and undergraduate courses on industrial ecology and sustainable firm strategy taught by the PIs at MIT and RIT. The ultimate goal is to provide students with the knowledge-base to improve their engineering decisions by understanding how their decisions will impact society. The outcomes of these efforts will be shared with the broader academic community through forums such as the Center for Sustainable Production at RIT and MIT?s OpenCourseWare, a free and accessible web-based publication of much of MIT?s course content. The post-docs employed within this project will receive mentoring through a structured program to improve teaching, presentation, publication, and fund-raising skills. The broader impact of this work stems from the collaborations it necessitates between industry and academia; this collaboration enables real-world implementation tests to ensure that the research leads to actionable methods not just abstract concepts. The outcomes of the work will be widely disseminated to audiences outside of traditional academic communities because of the industrial partnerships employed in the research. Overall, the outcomes of the work are targeted to benefit society by developing tools that seek to reduce the environmental impact of the process industries and create sustainable material systems.1133422 (Gaustad). This research will produce a systematic mapping of relative performance of deterministic and several forms of uncertainty-aware stochastic batch planning models for a range of production contexts. Specific contexts to be explored include at least three forms of batch qualityperformance functions (linear, power-law, and logarithmic) and four distributional forms of raw material (RM) quality (Lognormal, Max Extrema, Gamma, or Student's t). Additionally, this research will provide both analytical and quantitative case analysis that supports the economic and resource efficiency value of diversification of interdependence within industrial ecosystems. For three planned industrial case analyses, this research will quantify the distributional nature of RM quality and the current and potential ability to utilize secondary and renewable raw materials (SRRMs) while examining the impact of the number of raw materials attributes. The educational component of this research seeks to develop methods and case studies to incorporate sustainability into engineering education. These course materials will be integrated into graduate and undergraduate courses on industrial ecology and sustainable firm strategy taught by the PIs at MIT and RIT. The ultimate goal is to provide students with the knowledge-base to improve their engineering decisions by understanding how their decisions will impact society. The outcomes of these efforts will be shared with the broader academic community through forums such as the Center for Sustainable Production at RIT and MIT?s OpenCourseWare, a free and accessible web-based publication of much of MIT?s course content. The post-docs employed within this project will receive mentoring through a structured program to improve teaching, presentation, publication, and fund-raising skills. The broader impact of this work stems from the collaborations it necessitates between industry and academia; this collaboration enables real-world implementation tests to ensure that the research leads to actionable methods not just abstract concepts. The outcomes of the work will be widely disseminated to audiences outside of traditional academic communities because of the industrial partnerships employed in the research. Overall, the outcomes of the work are targeted to benefit society by developing tools that seek to reduce the environmental impact of the process industries and create sustainable material systems.

1133286 Kirchain/1133422 Gaustad这项研究将为一系列生产环境产生确定性和几种形式的不确定性感知随机批处理计划模型的相对性能的系统映射。要探索的具体背景包括至少三种形式的批次质量性能函数(线性、幂定律和对数)和四种原材料(RM)质量的分布形式(对数正态、最大极值、伽玛或学生t)。此外，这项研究将提供分析和定量案例分析，支持工业生态系统内相互依赖多样化的经济和资源效率价值。对于三个计划中的工业案例分析，本研究将量化资源管理质量的分布性质，以及当前和潜在的利用二次和可再生原材料(SRRM)的能力，同时检查原材料属性数量的影响。这项研究的教育部分试图开发将可持续发展纳入工程教育的方法和案例研究。这些课程材料将被整合到麻省理工学院和RIT的PIS教授的工业生态学和可持续企业战略的研究生和本科生课程中。最终目标是为学生提供知识基础，通过了解他们的决策将如何影响社会来改进他们的工程决策。这些努力的成果将通过论坛与更广泛的学术界分享，如麻省理工学院可持续生产中心和麻省理工学院S开放课程软件，这是一个免费和可访问的网络出版物，大部分麻省理工学院S课程内容。在这个项目中聘用的博士后将通过一个结构化的计划接受指导，以提高教学、演讲、出版和筹款技能。这项工作的更广泛影响来自它需要工业界和学术界之间的合作；这种合作使现实世界的实施测试能够确保研究产生可行的方法，而不仅仅是抽象的概念。由于研究中采用了产业合作伙伴关系，这项工作的成果将广泛传播给传统学术界以外的受众。总体而言，这项工作的成果旨在通过开发工具造福社会，这些工具寻求减少过程工业对环境的影响，并创造可持续的材料系统。这项研究将对确定性和几种形式的不确定性感知的随机批处理计划模型在一系列生产环境下的相对性能进行系统的映射。要探索的具体情况包括至少三种形式的批次质量性能函数(线性、幂定律和对数)和四种原材料(RM)质量的分布形式(对数正态、最大极值、伽玛或学生t)。此外，这项研究将提供分析和定量案例分析，支持工业生态系统内相互依赖多样化的经济和资源效率价值。对于三个计划中的工业案例分析，本研究将量化资源管理质量的分布性质，以及当前和潜在的利用二次和可再生原材料(SRRM)的能力，同时检查原材料属性数量的影响。这项研究的教育部分试图开发将可持续发展纳入工程教育的方法和案例研究。这些课程材料将被整合到麻省理工学院和RIT的PIS教授的工业生态学和可持续企业战略的研究生和本科生课程中。最终目标是为学生提供知识基础，通过了解他们的决策将如何影响社会来改进他们的工程决策。这些努力的成果将通过论坛与更广泛的学术界分享，如麻省理工学院可持续生产中心和麻省理工学院S开放课程软件，这是一个免费和可访问的网络出版物，大部分麻省理工学院S课程内容。在这个项目中聘用的博士后将通过一个结构化的计划接受指导，以提高教学、演讲、出版和筹款技能。这项工作的更广泛影响来自它需要工业界和学术界之间的合作；这种合作使现实世界的实施测试能够确保研究产生可行的方法，而不仅仅是抽象的概念。由于研究中采用了产业合作伙伴关系，这项工作的成果将广泛传播给传统学术界以外的受众。总体而言，这项工作的成果旨在通过开发工具来造福社会，这些工具寻求减少过程工业对环境的影响，并创建可持续的材料系统。