Studies of Stochastic Production Systems

随机生产系统的研究

• 批准号: 9908294
• 负责人: Xiuli Chao
• 金额: $ 14.53万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2001-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9908294&HistoricalAwards=false
• 关键词: Studies; Stochastic; Production; Systems

This grant provides funding for the development of computer-based mathematical models for finite-capacity production systems, including single-item models with random demand, multi-item models that share a critical capacitated resource, and supply chains with finite production capacity and random demands. For single item models the aim is to first determine the class of finite-capacity stochastic production systems for which the optimal policy has a simple form. The next goal is to determine a broad class of models whose cost function can be written in a form that has been successfully used in the study of inventory problems where items are ordered rather than manufactured. This special form of cost function guarantees, via sensitivity and worst case analyses, the robustness of heuristics obtained based on the solution of systems with deterministic demands, as well as distribution-free cost upper bounds. These heuristics will then be extended to multiple items sharing a critical capacitated resource and to multi-item, multi-stage systems with finite production capacity and random demands. The objective is to obtain structural and sensitivity analysis results for a wide variety of production and demand environments, and to use these results to develop efficient algorithms and near-optimal heuristics.If successful, the results of this research will lead to an improved understanding, and better ability to manage production and inventories in manufacturing systems facing random demands. Improved understanding of such systems will come from the sensitivity analysis of cost functions and from closed form cost upper bounds that reveal the cost impact of randomness in the worst case. Better ability to manage such systems will come from the development of computer based efficient algorithms and robust heuristics.

该补助金为有限能力生产系统开发基于计算机的数学模型提供资金，包括随机需求的单件模型，共享关键能力资源的多件模型，以及有限生产能力和随机需求的供应链。 对于单项目模型的目的是首先确定类的有限容量的随机生产系统的最优策略有一个简单的形式。下一个目标是确定一类广泛的模型，其成本函数可以写成一种形式，已成功地用于研究库存问题，其中物品是订购的，而不是制造。这种特殊形式的成本函数保证，通过灵敏度和最坏情况下的分析，基于确定性需求的系统的解决方案，以及分布免费的成本上界的鲁棒性获得。然后，这些物流将扩展到多个项目共享一个关键的能力有限的资源，并与有限的生产能力和随机需求的多项目，多阶段的系统。 其目的是获得各种各样的生产和需求环境的结构和灵敏度分析结果，并使用这些结果来开发有效的算法和接近最优的mistics.If成功，这项研究的结果将导致一个更好的理解，更好地管理生产和库存的制造系统面临的随机需求的能力。 对这种系统的更好的理解将来自于成本函数的敏感性分析和封闭形式的成本上限，这些上限揭示了最坏情况下随机性对成本的影响。 更好地管理这样的系统的能力将来自基于计算机的高效算法和鲁棒算法的发展。