NSF/EPA Partnership for Environmental Research: Environmentally Conscious Design and Manufacturing with Input-Analysis and Markovian Decision Making

* 9613076奥尔森 该奖项由NSF/EPA环境研究伙伴关系颁发。该研究探索并创建了一个数学计算工具，当与生命周期分析一起使用时，可以提供定量的影响改善分析。将创建工业产品或过程的定量状态空间模型。该模型，作为一个离散的有限马尔可夫链，将预测的影响，拟议的设计决策，并优化该决策的影响，可持续发展和环境的公司和它的行业。利用时间历史数据，将开发一个多变量数据依赖系统（DDS），以进一步完善分析，使决策者可以命令他们的决定，对他们的业务产生最小的破坏性影响。研究工具将在万维网上提供。 如果成功的话，这项研究将为工业界提供工具，使决策者能够在设施一级应用环境生命周期分析。目前，这些工具并不存在，限制或阻止决策者利用生命周期分析来规划产品和流程。在这些工具中，有一种有效的手段，可以计算由于试图满足特定的环境目标，以减少材料和防止污染，而导致的设施材料的预期变化。此外，这些工具提供了一个动态的，时间结构，增量决策的影响分析。通过比较和优化决策以满足行业竞争目标的能力，这一点得到了增强。这项研究扩展了生命周期分析，使制造商能够更好地量化其库存，并提供减少材料使用的途径。 ***

*** 9613076 Olson This award is made under the NSF/EPA Partnership for Environmental Research. The research explores and creates a mathematical computational tool which when used with Life Cycle Analysis provides a quantitative Impact Improvement Analysis. A quantitative state space model of industrial products or processes will be created. The model, as a discrete finite Markov chain, will predict the impact of a proposed design decision and optimize that decision for its impact upon both sustainable development and the environment on the firm and its industry. Using time history data, a multivariate data dependent system (DDS) will be developed to further refine the analysis so that decision makers can order their decisions to have the least disruptive effect upon their operations. The research tools will be made available on the World Wide Web. If successful, this research will provide to industry tools to allow decision makers to apply Environmental Life Cycle Analysis at the facility level. Currently these tools do not exist and restrict or prevent decision makers from taking advantage of Life Cycle Analysis in planning both products and processes. Among these tools is an effective means to compute the anticipated changes in materials on a facility due to the attempt to meet specified environmental goals for material reduction and prevention of pollution. Additionally, these tools provide a dynamic, time structured, analysis of the effects of incremental decisions. This is enhanced by the ability to compare and optimize decisions to meet industrial competitive objectives. This research extends Life Cycle Analysis to allow manufacturers to better quantify their inventories and to provide pathways to reducing material usage where desired. ***