Managing Perishable Inventory Systems: New Algorithms and Approximations

管理易腐烂库存系统：新算法和近似值

• 批准号: 1362619
• 负责人: Xiuli Chao
• 金额: $ 37.5万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1362619&HistoricalAwards=false
• 关键词: Managing; Perishable; Inventory; Systems; New

This grant provides funding for the development of simple, efficient, and near-optimal approximation algorithms for perishable stochastic inventory systems. Perishable products, such as fresh food, pharmaceuticals, and blood banks are ubiquitous and an indispensable part of our society, and spoilage and outdating represent a major threat to the profitability of companies such as grocery retailers. Thus, finding effective inventory management policies for perishable products is of significant importance. However, the analysis of dynamic perishable inventory systems is notoriously difficult in both theory and computation due to the high-dimensional nature. Indeed, the optimal control policies are very complex even in the case of independent and identically distributed demands, and the computation of optimal policies using dynamic program is in general intractable due to the "curse-of-dimensionality." The models studied allow general non-stationary and correlated demand processes, capturing the seasonality nature of the economy, and the approximation algorithms developed admit theoretical worst-case performance guarantees. If successful, the results of this research will lead to efficient tools for inventory managers to effectively incorporate demand forecast, such as advance demand information (ADI), martingale models of forecast evolution (MMFE), autoregressive moving average (ARMA) demand models, and Markov modulated demand process (MMDP), in making inventory replenishment decisions for perishable inventory systems. It will allow inventory managers to make use of available and reliable data, such as forecast updating, for inventory planning. The research will advance the science of stochastic inventory systems and provide deeper understanding of the subject area. The outcome of the project will help firms reduce waste, increase revenue, and even save lives (e.g., in blood bank applications).

该补助金为易腐随机库存系统的简单，高效和接近最优的近似算法的开发提供资金。易腐产品，如新鲜食品，药品和血库无处不在，是我们社会不可或缺的一部分，腐败和过时是对杂货零售商等公司盈利能力的主要威胁。因此，寻找有效的库存管理政策，易腐产品是非常重要的。然而，易腐物品动态库存系统的高维性使得其分析在理论和计算上都存在一定的困难。实际上，即使在独立同分布需求的情况下，最优控制策略也是非常复杂的，并且由于“维数灾难”，使用动态规划计算最优策略通常是困难的。“研究的模型允许一般的非平稳和相关的需求过程，捕捉经济的季节性，开发的近似算法承认理论上的最坏情况下的性能保证。如果成功的话，本研究的结果将导致有效的工具，库存管理人员有效地将需求预测，如提前需求信息（ADI），预测演化的鞅模型（MMFE），自回归移动平均（阿尔马）的需求模型，和马尔可夫调制的需求过程（MMDP），在易腐库存系统的库存补充决策。它将使库存管理人员能够利用现有的可靠数据，如预测更新，进行库存规划。 这项研究将推进随机库存系统的科学，并提供更深入的理解的主题领域。该项目的成果将帮助企业减少浪费，增加收入，甚至拯救生命（例如，在血库应用中）。