Capacity Expansion under Forecast Uncertainty: Stochastic Integer Programming Approaches

预测不确定性下的容量扩展：随机整数规划方法

• 批准号: 0099726
• 负责人: Shabbir Ahmed
• 金额: $ 11.76万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0099726&HistoricalAwards=false
• 关键词: Capacity; Expansion; under; Forecast; Uncertainty

The project is aimed at the development of optimization techniques for planning capacity expansions when forecasted planning data are unreliable. Stochastic programming has emerged as an important tool for solving planning problems with data uncertainties. In capacity expansion problems, however, the integral nature of strategic decisions prevents the use of standard decomposition approaches that have been successful for stochastic linear programs. This project will develop efficient solution strategies for stochastic integer programs arising in capacity expansion applications. A key component of the project will be to identify special problem structures that can be exploited within solution strategies. The structural results will be used to design, analyze, and implement approximate and exact solution algorithms. The viability of the developed methodology will be demonstrated in important economic sectors such, as semiconductor wafer fabrication facilities and web hosting enterprises.Capacity expansion to meet anticipated demand growth is a key strategic concern in all industrial sectors. In high growth-high volatility industries, such as the IT sector, uncertainties in forecasts for costs, demands, and technology evolution, and the economies-of-scale in expansion costs make capacity expansion decisions very complex. Using stochastic integer programming concepts, this research project will develop an optimization based paradigm for aiding capacity expansion that explicitly address forecast uncertainty. If successful, the project will provide robust computational techniques to aid strategic capacity planning in a wide variety of industries. It is also anticipated that insights gained from this research will significantly advance the current state-of-the-art in solving multi-stage stochastic integer programs.

该项目旨在开发在预测规划数据不可靠的情况下规划扩容的优化技术。随机规划已成为解决具有数据不确定性的规划问题的重要工具。然而，在产能扩张问题中，战略决策的整体性阻碍了标准分解方法的使用，而标准分解方法在随机线性规划中已经取得了成功。本项目将为产能扩展应用中出现的随机整数规划开发有效的解决策略。该项目的一个关键组成部分将是确定可以在解决方案策略中利用的特殊问题结构。结构结果将用于设计、分析和实现近似和精确解算法。开发方法的可行性将在重要的经济部门得到证明，如半导体晶圆制造设施和网络托管企业。扩大产能以满足预期的需求增长是所有工业部门关注的关键战略问题。在高增长-高波动的行业中，例如IT部门，成本、需求和技术演进预测中的不确定性，以及扩张成本中的规模经济，使得产能扩张决策非常复杂。利用随机整数规划概念，本研究项目将开发一种基于优化的范式，以帮助明确解决预测不确定性的产能扩张。如果成功，该项目将提供强大的计算技术，以帮助各种行业的战略能力规划。预计从本研究中获得的见解将大大推进当前解决多阶段随机整数计划的最新技术。