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: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=480832
• 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科学研究和实践的基本领域之一。在这一领域的主要研究问题是关于确定最佳库存和容量相关的决策，在广泛的情况下，这是具有重要意义的经济。这些决策使企业能够在正确的时间在正确的地点持有正确的库存。这些决策的重要性不容低估，因为加拿大近11%的国民生产总值用于物流活动，包括移动和存储实物库存。在当今燃料价格不断上涨的经济环境下，做出最佳决策和减少浪费尤为重要。 因此，在这一领域的良好研究往往直接帮助从业者降低成本，提高盈利能力，改善社会剩余。尽管它作为一个研究领域已经成熟，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，其中很少有人知道的结构或算法计算接近最优的政策。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容易实现的策略，这些策略将在广泛的现实情况下产生接近最佳性能的结果，并且通常提供对最坏情况性能的理解。在这个过程中，最优策略的重要性质也将得到理解。我特别感兴趣的设置包括多个采购，易腐产品，大规模多阶段的能力限制系统，多个产品是彼此的部分替代品，并与能力限制和随机产量的设置。