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 GAUSTADHIS研究将对确定性和几种形式的不确定性感知随机批处理批处理计划模型进行系统映射，以构成一系列生产环境。要探索的特定上下文包括至少三种形式的批次质量性能功能（线性，幂律和对数）和四种原材料（RM）质量的分配形式（lognormal，Max ofarsa，gamma，gamma或Student's T）。此外，这项研究将提供分析和定量案例分析，以支持工业生态系统中相互依存多样化的经济和资源效率价值。对于三个计划的工业案例分析，这项研究将量化RM质量的分布性质以及使用二次和可再生原材料（SRRMS）的当前和潜在能力，同时研究原材料属性数量的影响。 这项研究的教育部分旨在开发方法和案例研究，以将可持续性纳入工程教育。这些课程材料将纳入有关MIT和RIT的PIS教授的工业生态学和可持续公司战略的研究生和本科课程。最终目标是通过了解他们的决策将如何影响社会，为学生提供知识库，以改善其工程决策。这些努力的结果将通过论坛与更广泛的学术界共享，例如RIT和MIT的可持续生产中心，这是MIT大部分课程内容的免费且易于访问的基于网络的出版物。该项目中使用的后DOC将通过结构化计划获得指导，以提高教学，演示，出版和筹款技能。这项工作的广泛影响源于行业和学术界之间需要的合作。该协作使实际实施测试可以确保研究导致可行的方法不仅是抽象概念。由于研究中采用的工业伙伴关系，这项工作的结果将被广泛传播给传统学术社区以外的观众。总体而言，这项工作的结果是通过开发旨在减少工艺行业的环境影响并创建可持续材料系统的工具来使社会受益的目标。1133422（Gaustad）。这项研究将对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进行系统的映射。要探索的特定上下文包括至少三种形式的批次质量性能函数（线性，幂律和对数）和四种原材料（RM）质量的分布形式（logNormal，Max obs oxtreara，gamma或Student's T）。此外，这项研究将提供分析和定量案例分析，以支持工业生态系统中相互依存多样化的经济和资源效率价值。对于三个计划的工业案例分析，这项研究将量化RM质量的分布性质以及使用二次和可再生原材料（SRRMS）的当前和潜在能力，同时研究原材料属性数量的影响。 这项研究的教育部分旨在开发方法和案例研究，以将可持续性纳入工程教育。这些课程材料将纳入有关MIT和RIT的PIS教授的工业生态学和可持续公司战略的研究生和本科课程。最终目标是通过了解他们的决策将如何影响社会，为学生提供知识库，以改善其工程决策。这些努力的结果将通过论坛与更广泛的学术界共享，例如RIT和MIT的可持续生产中心，这是MIT大部分课程内容的免费且易于访问的基于网络的出版物。该项目中使用的后DOC将通过结构化计划获得指导，以提高教学，演示，出版和筹款技能。这项工作的广泛影响源于行业和学术界之间需要的合作；该协作使实际实施测试可以确保研究导致可行的方法不仅是抽象概念。由于研究中采用的工业伙伴关系，这项工作的结果将被广泛传播给传统学术社区以外的观众。总体而言，这项工作的成果旨在通过开发旨在减少工艺行业的环境影响并创建可持续材料系统的环境影响的工具来使社会受益。