Production Strategies for Batch Processing with Random Yield

随机产量批量处理的生产策略

• 批准号: 9713727
• 负责人: Tava Olsen
• 金额: $ 26.22万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-10-01 至 2002-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9713727&HistoricalAwards=false
• 关键词: Production; Strategies; Batch; Processing; Random

Olsen 9713727 This grant provides funding for the study of batch production environments with random yield. In these environments, products are produced in batches because either setup times or setup costs between product types are significant. Furthermore, the output from production is assumed to be highly variable. The motivation for this work is from the automotive parts industry and particularly a plant which produces molded and painted plastic bumpers. The paint line in this plant has highly unpredictable yield and changeovers between different paint colors are expensive. This research will further the development of (a) models that take into account random yield in a continuous time environment; (b) solution methods for these models that achieve computable error bounds on optimal values using both discrete and continuous approximations; (c) characterizations of general properties of optimal solutions of these random yield models that provide insight into managerial decisions; and (d) comparisons of discrete and continuous time models in general with insight into fundamental model characteristics of each approach and their relationship to underlying situations. The main impact of the research will be to improve decision making in random yield systems. It will provide methods that have computable bounds and that can be applied in a variety of settings from machining to painting to semiconductor fabrication. The methodology will be tested on real data to ensure that it is implementable in real time in complex manufacturing environments. The results of this research are expected to have a significant effect on industries critical to U.S. competitiveness (such as the automotive parts industry) where product yield is highly variable. The investigators will work with interested companies to ensure that the results are relevant. The research is also expected to have significant academic contributions in the areas of queueing theory, scheduling, and stochastic programming.

奥尔森9713727 该补助金为随机产量的批量生产环境的研究提供资金。 在这些环境中，产品是批量生产的，因为产品类型之间的设置时间或设置成本很大。 此外，生产的产量 被认为是高度可变的。 这项工作的动机来自汽车零部件行业，特别是一家生产模制和涂漆塑料保险杠的工厂。 这家工厂的油漆生产线产量和不同油漆颜色之间的转换非常不可预测 都很贵 这项研究将进一步发展（a）在连续时间环境中考虑随机产量的模型;（B）这些模型的求解方法，这些方法使用离散和连续近似法在最优值上实现可计算的误差界;（c） 这些随机收益率模型最优解的一般属性的表征，为管理决策提供见解;以及（d）对离散和连续时间模型进行一般比较，深入了解每种方法的基本模型特征及其与潜在情况的关系。 该研究的主要影响将是改善随机产量系统中的决策。 它将提供具有可计算边界的方法，并且可以应用于从机加工到油漆到半导体制造的各种设置中。 这一方法将 在真实的数据上进行测试，以确保它在复杂的制造环境中可以真实的实时实现。 这项研究的结果预计将对美国竞争力至关重要的行业（如汽车零部件行业）产生重大影响，这些行业的产品产量变化很大。 调查人员将与感兴趣的公司合作，以确保结果是相关的。 该研究还有望在以下领域做出重大学术贡献： 排序理论、排程与随机规划。