Designing and Controlling Processing Networks with Parameter Uncertainty

这笔拨款提供资金，用于研究结合排队网络动力学和随机规划决策框架的模型。在运筹学及相关领域，对排队网络和参数不确定模型的研究已有很长的历史，但对同时包含这两个特征的模型进行系统分析的工作相对较少。在研究的第一部分，分析了一个流体加工网络，其中流体到达率、加工率和初始库存都不是完全确定的。控制器感兴趣的是最小化预期库存成本，因为某些结构性经营决策必须在上述随机因素被揭示之前做出。然后，这个流体模型将被用来为具有离散作业的随机处理网络制定设计和控制策略，其中存在参数不确定。在研究的最后阶段，控制政策将在半导体晶片制造设施的全尺寸、详细的模拟模型中进行测试。模拟模型将与国际SEMATECH合作开发。作为这项工作的教学贡献的一部分，研究成果将通过随机编程和随机处理网络的教程在随机编程社区的主页上传播。如果成功，该项目将为排队论、随机编程和制造业研究社区做出贡献。在排队社区中，历史上的焦点通常是性能分析和控制，要么假设所有系统参数已知，要么假设控制器可以即时适应参数的变化。目前，很少有研究采取系统的方法将参数不确定性纳入这些模型中。在许多随机规划模型中，目标是在更详细操作的粗粒子模型的指导下做出战术或战略级别的决策。本项目所研究的排队网络就是这些子模型的理想候选者。在行业环境中，这项研究将使制造业和其他行业的网络设计师和控制器能够在面临不确定性的情况下做出更好的决策。由于这个项目包括对半导体行业的深入参与，这项研究将对该领域产生最直接的影响。然而，在电信和交通管理等许多需要随机网络控制的部门，有很大可能产生广泛影响。

This grant provides funding to examine models which combine queueing networkdynamics and a stochastic programming decision framework. The study of both queueing networks and models with parameter uncertainty have a long history in operations research and related areas, but there has been relatively little work on systematically analyzing models which contain both features. In the first part of the research, a fluid processing network is analyzed in which the fluid arrival rates, processing rates, and initial inventory are not known with complete certainty. The controller is interested in minimizing expected inventory costs, given that certain structural operating decisions must be made before the aforementioned random elements are revealed. This fluid model will then be used to develop design and control policies for stochastic processingnetworks, with discrete jobs, in which there is parameter uncertainty. In the final phase of the research the control policies will be tested in full-scale, detailed simulation models of semiconductor wafer fabrication facilities. The simulation models will be developed in collaboration with International SEMATECH. As part of the pedagogical contribution of this work, the research results will be disseminated on the Stochastic Programming Community Homepage via a tutorial on stochastic programming and stochastic processing networks.If successful, this project will make contributions to the queueing theory, stochastic programming, and manufacturing research communities. In the queueing community the historical focus has usually been on performance analysis and control, under either the assumption that all system parameters are known or that a controller can instantaneously adapt to changing parameters. At present, there is little research which takes a systematic approach to incorporating parameter uncertainty into these models. In many stochastic programming models, the goal is to make tactical- or strategic-level decisions with the guidance of a coarse-grain submodel of more detailed operations. The queueing networks studied in this project are ideal candidates for such submodels. In the industry setting, the research will allow network designers and controllers in the manufacturing sector, and beyond, to make better decisions in the face of uncertainty. Since this project includes in-depth involvement with the semiconductor industry, the research will have the most immediate impact in this sector. However, there is strong potential for broad impact in many sectors, such as telecommunications and traffic management, where stochastic network control is necessary.