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.

随机库存管理是运筹学和管理学的基本研究和实践领域之一。这一领域感兴趣的主要研究问题是在对经济具有重要意义的各种情况下确定与库存和产能有关的最优决策。这些决策使公司能够在正确的时间在正确的地点持有正确的库存。不能低估这种决定的重要性，因为加拿大国民生产总值的近11%花在涉及移动和储存实物库存的后勤活动上。在当今燃料价格不断上涨的经济气候下，做出最佳决策和减少浪费尤为关键。因此，这一领域的良好研究往往直接帮助从业者削减成本，提高盈利能力，改善社会盈余。尽管它是一个成熟的研究领域，但到今天为止，仍有大量的问题设置构成了重大的理论和实践挑战。在这项研究中，我建议研究一组库存问题，关于计算近最优策略的结构或算法，人们对这些问题知之甚少。对于这些问题，我的研究将构建近似算法，这些算法将产生相对容易实现的策略，在广泛的现实情况下产生接近最佳性能的策略，并经常提供对最差情况性能的理解。在这个过程中，最优策略的重要性质也将被理解。我特别感兴趣的设置包括多来源、易腐烂的产品、大规模的多阶段能力限制系统、相互部分替代的多产品和具有能力约束的设置以及随机产量的设置。