权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

GOALI: Large Scale Dynamic Programming for Optimizing the Design and Operation of Complex Systems with Applications to Production Line Design

GOALI：用于优化复杂系统设计和操作的大规模动态规划及其在生产线设计中的应用

基本信息

批准号：
9900267
负责人：
Robert Smith
金额：
$ 20.51万
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：
1999
资助国家：
美国
起止时间：
1999-08-01 至 2004-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9900267&HistoricalAwards=false
关键词：
GOALI Large Scale Dynamic Programming

项目摘要

Dynamic programming is an extremely general framework for the formulation and solution of nonlinear discrete optimization problems. It allows for the explicit incorporation of either-or decisions as well as the effects of underlying uncertainty that may be present in the system being modeled. Its principal drawback is the tendency for the underlying state space of the formulation to grow explosively with respect to the size of the system. This grant provides funding for the development of procedures to limit to manageable proportions the growth of the state space that needs to be considered. This will be accomplished in two ways: (1) Model Simplification though hierarchical state aggregation; and (2) Solution Acceleration through state pruning. The former approach can introduce errors which will be controlled through the ability to compute bounds on the resulting error of the approximation. The models and algorithms developed will be implemented and validated on a large scale problem in production line design in cooperation with research staff at the General Motors R&D Center at Warren, Michigan. If this research effort is successful, the resulting ability to approximately model and optimize large scale complex systems with easily implemented dynamic programming techniques will greatly expand the class of design and operational problems in manufacturing and service sectors that are amenable to analysis.

动态规划是一个非常通用的框架，制定和解决非线性离散优化问题。它允许明确纳入非此即彼的决定，以及潜在的不确定性的影响，可能存在于系统建模。它的主要缺点是公式的底层状态空间倾向于相对于系统的大小爆炸性增长。这笔赠款为制定程序提供资金，以将需要考虑的州空间的增长限制在可管理的比例。这将通过两种方式实现：（1）通过分层状态聚合的模型简化;以及（2）通过状态修剪的解决方案加速。前一种方法可能会引入误差，这些误差将通过计算近似结果误差的界限的能力来控制。开发的模型和算法将在生产线设计中的大规模问题上与密歇根州沃伦的通用汽车研发中心的研究人员合作实施和验证。如果这项研究工作是成功的，所产生的能力，近似模型和优化大规模的复杂系统，易于实施的动态规划技术将大大扩展类的设计和操作问题，在制造业和服务部门，是经得起分析。