University-Industry Graduate Research Assistantships for Developing Analytical Models to Estimate Performance Measures and Optimize Resource Allocation in a Complex Server Manufact

大学-行业研究生研究助理奖学金，用于开发分析模型，以估计复杂服务器制造商的性能指标并优化资源分配

• 批准号: 0739197
• 负责人: Purushothaman Damodaran
• 金额: $ 2.89万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-15 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0739197&HistoricalAwards=false
• 关键词: University; Industry; Graduate; Research; Assistantships

This research initiative under the University-Industry Cooperative Research Programs in the Mathematical Sciences (UICRP) is aimed to develop analytical models to estimate the performance measures of a complex server manufacturing system. The server manufacturing system will be modeled as a network of queues and the performance measures will be estimated using parametric decomposition. A simulation model will also be developed to estimate the performance measures. The error between the analytical and simulation models will be compared to determine the accuracy of the proposed analytical models. An accurate analytical model can eliminate the need to develop simulation models. The performance measure estimates can be used with a capacity planning formulation to optimize the number of resources at the assembly / test stages. Consequently, the time required to fill an order can be minimized, the utilization of the various resources across the facility can be improved, and cost due to missed deadlines can be avoided. Jointly mentored by the university and industry researchers, the graduate students will develop the models and solution approaches which will benefit server manufacturers and other applications which resemble server manufacturing.

在数学科学大学-工业合作研究计划（UICRP）下的这项研究计划旨在开发分析模型，以评估复杂服务器制造系统的性能指标。服务器制造系统将被建模为一个队列网络，性能度量将使用参数分解来估计。还将开发一个模拟模型来估计性能措施。分析模型和仿真模型之间的误差将进行比较，以确定所提出的分析模型的准确性。一个准确的分析模型可以消除开发仿真模型的需要。性能度量估计可以与容量规划公式一起使用，以优化装配/测试阶段的资源数量。因此，完成订单所需的时间可以最小化，整个设施的各种资源的利用率可以得到改善，并且可以避免由于错过最后期限而导致的成本。在大学和行业研究人员的共同指导下，研究生将开发模型和解决方案方法，这些模型和解决方案将使服务器制造商和其他类似服务器制造的应用受益。