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

1133286（Kirchain）。这项研究将产生一个系统的映射的相对性能的确定性和几种形式的不确定性感知的随机批量计划模型的范围内的生产环境。具体的情况下，探索包括至少三种形式的批量质量性能函数（线性，幂律和对数）和四种分布形式的原材料（RM）质量（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存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RM质量的分布性质以及利用二次和可再生原材料（SRRM）的当前和潜在能力，同时研究原材料属性数量的影响。 本研究的教育部分旨在开发方法和案例研究，将可持续性纳入工程教育。这些课程材料将被整合到研究生和本科生课程的工业生态学和可持续发展的企业战略教授的PI在麻省理工学院和RIT。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善他们的工程决策。这些努力的成果将通过RIT和MIT的可持续生产中心等论坛与更广泛的学术界分享。开放式课程，一个免费的和可访问的基于网络的出版物的大部分麻省理工学院？的课程内容。在这个项目中雇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。这项工作更广泛的影响源于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，以确保研究导致可操作的方法，而不仅仅是抽象的概念。由于研究中采用了工业伙伴关系，工作成果将广泛传播给传统学术界以外的受众。总体而言，工作成果旨在通过开发旨在减少加工工业对环境影响和创建可持续材料系统的工具来造福社会。

1133286 (Kirchain). 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.