Dynamic Resource Allocation in Stochastic Processing Networks

随机处理网络中的动态资源分配

• 批准号: 0300599
• 负责人: Jiangang Dai
• 金额: $ 30万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2006-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0300599&HistoricalAwards=false
• 关键词: Dynamic; Resource; Allocation; Stochastic; Processing

Complex systems like semiconductor wafer fabrication facilities (fabs), networks of data switches, and large scale call centers all demand efficient resource allocation. Deterministic models like linear programs (LP) have been used for capacity planning at both the design and expansion stages of such a system. LP-based planning is critical in setting a medium range or long term goal for many systems. But it does not translate into a day-to-day operational policy that must deal with discreteness of jobs and the randomness of the processing environment. This research project will investigate a general class of stochastic processing networks. A processing network is a system that takes inputs of materials of various kinds and uses various processing resources to produce outputs of materials of various kinds. These processing networks provide high-fidelity stochastic models in diverse economic sectors including manufacturing, service and information technology. The key goal of this research is to devise dynamic, operational policies that can achieve long term objectives for networks. These objectives include (i) achieving maximum throughput predicted by LPs, and furthermore, (ii) minimizing work-in-process or delays in networks. The research team will make fundamental understanding of these stochastic processing networks by building mathematical frameworks at all three time and space scales. More importantly, the team will use the frameworks to (i) devise operational policies that require minimal state information; (ii) prove that these policies are throughput optimal; (iii) identify and prove that policies are asymptotically optimal in terms of some second order performance measures when the system has a unique pooled bottleneck station; (iv) adapt the theoretically proven policies so that they can be readily implemented in a number of application areas.The project attempts to solve dynamic resource allocation problems that are challenging in different economic sectors including manufacturing, service and information technology. The results discovered from this research can directly be applied to wafer fab scheduling, input-queued data switch design and control, Internet Border Gateway routing, congestion based road traffic pricing/control, and call center scheduling. These applications intersect with many disciplines including electrical engineering, computer science, civil engineering, industrial engineering, and management. The Ph.D. students who conduct research on the project will have excellent interdisciplinary training. These interdisciplinary skills are essential for future academic leaders. Building on proven track record, the PI will turn cutting edge research results into course materials to be used for both graduate and undergraduate students at Georgia Tech and elsewhere.

半导体晶圆制造厂(FABS)、数据交换网络和大型呼叫中心等复杂系统都需要高效的资源分配。像线性规划(LP)这样的确定性模型已被用于此类系统的设计和扩展阶段的容量规划。在为许多系统设定中期或长期目标时，基于LP的规划至关重要。但这并不意味着必须处理工作的离散性和处理环境的随机性的日常操作政策。本研究项目将研究一类一般的随机处理网络。加工网络是接受各种材料的输入，并利用各种加工资源来产生各种材料的输出的系统。这些处理网络在包括制造业、服务业和信息技术在内的不同经济部门提供高保真随机模型。本研究的主要目标是设计动态、可操作的策略，以实现网络的长期目标。这些目标包括(I)实现LP预测的最大吞吐量，以及(Ii)最大限度地减少网络中的在制品或延迟。研究小组将通过在所有三个时间和空间尺度上建立数学框架来对这些随机处理网络进行基本了解。更重要的是，该团队将使用这些框架来：(I)设计需要最少状态信息的操作策略；(Ii)证明这些策略是吞吐量最优的；(Iii)当系统具有唯一的共享瓶颈站时，识别并证明策略在某些二阶性能指标方面是渐近最优的；(Iv)调整理论上经过验证的策略，以便它们可以容易地在许多应用领域实施。该项目试图解决在不同经济部门(包括制造业、服务业和信息技术)中具有挑战性的动态资源分配问题。研究结果可直接应用于晶圆厂调度、输入排队数据交换设计与控制、互联网边界网关路由、基于拥塞的道路交通定价/控制，以及呼叫中心调度。这些应用程序与许多学科交叉，包括电气工程、计算机科学、土木工程、工业工程和管理。从事该项目研究的博士生将接受出色的跨学科培训。这些跨学科技能对于未来的学术领袖来说是必不可少的。在经过验证的记录的基础上，PI将把尖端研究成果转化为课程材料，供佐治亚理工学院和其他地方的研究生和本科生使用。