New mathematical programming approaches for assemble-to-order system optimization

用于按订单组装系统优化的新数学编程方法

• 批准号: 386124-2010
• 负责人: Huang, Kai
• 金额: $ 1.89万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=528742
• 关键词: New; mathematical; programming; approaches; assemble

Assemble-To-Order (ATO) systems represent a large class of important manufacturing systems in the contemporary economy, especially in the high-tech sector. The optimization of ATO system operations involves difficult stochastic optimization problems. Due to this difficulty, there is a big gap between the industrial practice and the fast growing academic literature on this topic. In this research, we develop a conceptual innovation called "multi-matching" on characterizing the ATO systems. Starting from this innovation, we propose new stochastic programming models that can incorporate the critical stochastic elements in ATO systems, while embracing the computational power of mathematical programming. We aim at solving small- to medium-sized instances accurately, and providing good solutions with performance guarantee for large-scale systems in real life. The proposed approaches have several important implications in both academia and industry. Firstly, the stochastic programming models inject new insights into the ATO systems, and can provide a uniform benchmark for the practice in the industry and the existing heuristics in the literature. Secondly, the conceptual innovation and the corresponding mathematical programs can be applied to very general inventory systems. They can be used to study the impact of customer-differentiated service measures, supply risks, stochastic lead times and yield uncertainty. They can also be extended to multi-product and multi-echelon inventory systems. Thirdly, this research provides an important application of the state-of-the-art stochastic programming techniques, and brings new challenges and opportunities to both the modeling and computation of mathematical programming. The proposed research program will train Ph.D.'s with the expertise in inventory theory, stochastic programming / mathematical programming, and ERP software, and M.Sc.'s with the expertise in real life ATO systems, and ERP software.

按订单装配（ATO）系统代表了现代经济中的一大类重要制造系统，特别是在高科技领域。ATO系统运行优化涉及到困难的随机优化问题。由于这一困难，在工业实践和快速增长的关于这一主题的学术文献之间存在很大差距。在这项研究中，我们开发了一个概念上的创新，称为“多匹配”的ATO系统的特点。从这一创新开始，我们提出了新的随机规划模型，可以将ATO系统中的关键随机元素，同时拥抱数学规划的计算能力。我们的目标是准确解决中小型实例，并为真实的生活中的大型系统提供具有性能保证的良好解决方案。所提出的方法在学术界和工业界都有几个重要的影响。首先，随机规划模型为ATO系统注入了新的见解，可以为行业实践和文献中现有的物流学提供统一的基准。其次，概念创新和相应的数学规划可以应用于非常一般的库存系统。它们可用于研究客户差异化服务措施、供应风险、随机交货期和收益不确定性的影响。它们也可以扩展到多产品和多级库存系统。第三，本研究为最新的随机规划技术提供了一个重要的应用，给数学规划的建模和计算带来了新的挑战和机遇。该研究计划将培养博士生。拥有库存理论、随机规划/数学规划和ERP软件方面的专业知识，的专业知识，在真实的生活ATO系统，和ERP软件。