Approximation algortihms for stochastic inventory models

随机库存模型的近似算法

• 批准号: 299193-2009
• 负责人: Nagarajan, Mahesh
• 金额: $ 3.28万
• 依托单位: University of British Columbia
• 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=526114
• 关键词: Approximation; algortihms; stochastic; inventory; models

Stochastic inventory management is one of the fundamental areas of research and practice in operations management and management science. The main research questions of interest in this field are about determining optimal inventory and capacity related decisions in a wide set of instances which are of great significance to the economy. These decisions enable firms to hold the right inventory at the right location at the right time. The importance of such decisions cannot be underestimated as almost 11% of the Canadian GNP is spent on logistical activities that involve moving and storing physical inventory. In today's economic climate with rising fuel prices, making optimal decisions and reducing waste is particularly crucial. Thus, good research in this field often directly helps practitioners cut costs and improve profitability and improve social surplus. Despite its maturity as a research area, as of today, there are large classes of problem settings which pose significant theoretical and practical challenges. In this research, I propose to study a set of inventory problems, for which little is known in terms of the structure or algorithms that compute the near-optimal policies. For these problems, my research will construct approximation algorithms which will result in policies that are relatively easy to implement, that will yield close to optimal performance under a wide set of realistic instances and often offer an understanding of the worst case performance. In this process, important properties of the optimal policy will also be understood. The settings I am particularly interested include multiple sourcing, perishable products, large scale multi stage capacitated systems, multiple products which are partial substitutes of each other and with capacity constraints and settings with random yield.

随机库存管理是运营管理和管理科学研究和实践的基本领域之一。该领域感兴趣的主要研究问题是在广泛的实例中确定最佳库存和与容量相关的决策，这对经济具有重要意义。这些决定使公司能够在正确的时间在正确的位置保存正确的库存。这种决定的重要性不能被低估，因为加拿大GNP的几乎11％用于涉及移动和存储身体库存的后勤活动。在当今的经济环境中，燃油价格上涨，做出最佳决策和减少废物尤为重要。 因此，在这一领域的良好研究通常直接有助于从业者降低成本并提高盈利能力并改善社会盈余。尽管它是研究领域的成熟度，但到目前为止，仍有大量的问题设置构成了重大的理论和实际挑战。 在这项研究中，我建议研究一系列库存问题，从计算近乎最佳政策的结构或算法方面，几乎没有知名度。对于这些问题，我的研究将构建近似算法，这些算法将导致相对易于实施的政策，这些政策将在广泛的现实实例下产生接近最佳性能，并经常能够理解最坏情况的性能。在此过程中，最佳政策的重要属性也将被理解。我特别感兴趣的设置包括多种采购，可腐烂的产品，大规模多阶段电容系统，多种产品，这些产品是彼此的部分替代品，以及具有随机产量的容量限制和设置。