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不确定环境下汽车整车订单承诺方法研究 | |
Alternative Title | Research on Automobile Order Promising Method Under Uncertainty Environment |
孙德厂1,2![]() | |
Department | 数字工厂研究室 |
Thesis Advisor | 史海波 |
Classification | U462 |
Keyword | 订单承诺 可承诺量 可承诺能力 随机规划 交货期 支持向量回归 |
Call Number | U462/S96/2013 |
Pages | 105页 |
Degree Discipline | 机械电子工程 |
Degree Name | 博士 |
2013-05-25 | |
Degree Grantor | 中国科学院沈阳自动化研究所 |
Place of Conferral | 沈阳 |
Abstract | 从分析汽车整车生产模式、订单管理流程、交付周期模型切入,在现有订单承诺模型框架的基础上,构建面向ATO生产模式下订单承诺框架。在框架内对相关理论、模型、技术和方法进行研究,并通过实证数据验证和分析,表明其有效性和可行性。本文对相关理论和技术进行了基础研究,用于问题适用性修订,并进行了相关数据实验和分析。本文重点研究具有不确定性特征的ATO 生产模式下汽车整车订单承诺方法。不确定性的定量分析来源于历史生产过程数据,并构建随机规划模型、不确定性支持向量回归等方法,用于订单数量承诺和交货期设置,这些都有别于传统的确定性方法。在计划决策阶段,就充分考虑这些要素的量化影响程度比定性的不确定性处理技术更为准确和可靠。 |
Other Abstract | In this paper, beginning with the analysis of the production model, order management process, delivery cycle model of the automobile, based on existing order promising framework, we build the ATO-oriented order promising framework, and research relevant theories, models, techniques and methods within the framework. By the empirical data validation and analysis demonstrate its effectiveness and feasibility. This paper focuses on the uncertainty characterized ATO production mode automobile order promising problem. The quantitative analysis of uncertainty from the history of the production process data, and build stochastic programming model, uncertainty support vector regression and other methods, for commitments the quantity and due date of orders, which are different from the traditional deterministic approach. In the stage of planning and decisions, fully taking into account of the quantitative impact of these elements are more accurate and reliable when order promising. |
Language | 中文 |
Contribution Rank | 1 |
Document Type | 学位论文 |
Identifier | http://ir.sia.cn/handle/173321/10731 |
Collection | 数字工厂研究室 |
Affiliation | 1.中国科学院沈阳自动化研究所 2.中国科学院大学 |
Recommended Citation GB/T 7714 | 孙德厂. 不确定环境下汽车整车订单承诺方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2013. |
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不确定环境下汽车整车订单承诺方法研究.p(3260KB) | 开放获取 | CC BY-NC-SA | Application Full Text |
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