Dynamic, Concave-cost, and Lattice Programming with Application to Inventory Control and Scheduling

动态、凹成本和格子规划在库存控制和调度中的应用

• 批准号: 9215337
• 负责人: Arthur Veinott, Jr.
• 金额: $ 19.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-01 至 1996-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9215337&HistoricalAwards=false
• 关键词: Dynamic; Concave; cost; Lattice; Programming

This project will continue work on dynamic, lattice, d-Schur- convex-cost and concave-cost programming with applications to inventory control, scheduling, networks, and multiperson decisions. The theory of lattice programming and of substitutes, complements and ripples in network flows will continue to be developed to enable one to predict the direction of changes of optimal and equilibrium decisions resulting from changes in parameters without computation. This work will be continued and extended in a variety of significant directions including stochastic network flows, Leontief substitution systems and the development of efficient algorithms. Decision problems in which there are economies-of- scale lead to minimum-concave-cost mathematical programs. These problems will continue to be studied with the aim of identifying broad classes of problems that can be solved in polynomial time. Important problems of sequential decision making under uncertainty can be studied as Markov branching decision process. A variety of problems will be studied in this area including controlled populations, geometric population growth, complexity of computation and generalized semi-Markov decision processes. Special attention will be focused on developing approximation methods with guaranteed high-effectiveness and fast running times for large-scale systems. This work will be applied to yield management, single- and multi-product/echelon/facility inventory models under uncertainty, and other areas.

该项目将继续研究动态，晶格，D-Schur-Convex-Cost和凹入成本的编程，并将其应用于库存控制，调度，网络和多人物决策。 晶格编程的理论以及网络流中的替代，补充和涟漪的理论将继续开发，以预测由于参数变化而没有计算而导致的最佳和平衡决策变化的方向。 这项工作将继续以各种重要方向进行，包括随机网络流，Leontief替代系统和有效算法的发展。 存在经济经济的决策问题导致最低限度的数学计划。 这些问题将继续进行研究，目的是确定可以在多项式时间内解决的广泛问题。 在不确定性下进行顺序决策的重要问题可以作为马尔可夫分支决策过程。 该领域将研究各种问题，包括受控人群，几何种群增长，计算的复杂性和广义的半马尔可夫决策过程。 特别关注的关注将集中在开发具有高效率和快速运行时间的大规模系统的近似方法上。 这项工作将应用于不确定性和其他领域下的单产管理，单品和多产品/梯队/设施库存模型。