Computational and Applied Aspects of Multiperiod Stochastic Programming

多周期随机规划的计算和应用方面

基本信息

项目摘要

9523275 Birge This research is a continuation of a previous NSF funded project on stochastic programming. Specifically, this project will continue the development of solution methods, approximation procedures, models , and structural results for multiperiod stochastic programs. These objectives will be accomplished through the development of new decomposition methods for distributed processors, including integer variables and nonlinear functions, new methods with interior point techniques that focus on distributed solutions, new bounds on the value of the stochastic solution and approximation solutions, new techniques for bounding stochastic integer programming problems, and new approximations and methods using specific model characteristics to enable accurate results. Cmmparisons will consider alternative approaches with computational effort, error analysis, and the value of information and model complexity. The models will be applied to a variety of problems drawn from manufacturing, finance, vehicle routing, power systems planning, and energy policy. The goals are to obtain efficient practical solutions with known error characteristics. Many optimization problems are characterized by parameter values that are not known with certainty. Stochastic programming recognizes the uncertain nature of parameters to model and solve problems. The algorithms developed in this research will provide additional capabilities for solving complex decision problems which generally possess greater number of parameters with uncertain values. The explicit recognition of uncertainty in model building combined with more efficient algorithms for solving the models will lead to higher decision quality. The impact of improved decision quality from an economic point of view can be very significant. Further, the models investigated and the solution techniques produced will help to advance the knowledge in the area.
本研究是对先前国家科学基金资助的随机规划项目的延续。具体地说,这个项目将继续发展多周期随机程序的解方法、近似程序、模型和结构结果。这些目标将通过开发分布式处理器的新分解方法来实现,包括整数变量和非线性函数,专注于分布式解的内点技术的新方法,随机解和近似解的值的新边界,随机整数规划问题的边界新技术,新的近似和方法使用特定的模型特征,使准确的结果。比较将考虑计算工作量、错误分析、信息价值和模型复杂性的替代方法。这些模型将应用于制造、金融、车辆路线、电力系统规划和能源政策等领域的各种问题。目标是获得具有已知误差特性的有效实用解。许多优化问题的特点是参数值是不确定的。随机规划认识到参数的不确定性来建模和解决问题。本研究中开发的算法将为解决复杂决策问题提供额外的能力,这些问题通常具有大量不确定值的参数。在模型建立过程中对不确定性的明确识别,结合更高效的模型求解算法,将提高决策质量。从经济角度来看,提高决策质量的影响是非常显著的。此外,所研究的模型和所产生的解决方案技术将有助于推进该领域的知识。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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John Birge其他文献

Modeling manager confidence in forecasted excess returns under active portfolio management
  • DOI:
    10.1057/jam.2014.36
  • 发表时间:
    2014-10-23
  • 期刊:
  • 影响因子:
    1.400
  • 作者:
    John Birge;Luis Chavez-Bedoya
  • 通讯作者:
    Luis Chavez-Bedoya

John Birge的其他文献

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{{ truncateString('John Birge', 18)}}的其他基金

Collaborative Research: Managing Material Flow and Cash Flow in the Supply Chain
合作研究:管理供应链中的物流和现金流
  • 批准号:
    0100462
  • 财政年份:
    2001
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Standard Grant
Intelligent Unified Control of Unit Commitment and Generation Allocation
机组承诺和发电分配智能统一控制
  • 批准号:
    9216819
  • 财政年份:
    1993
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Continuing Grant
Computational and Applied Aspects of Multiperiod Stochastic Programming
多周期随机规划的计算和应用方面
  • 批准号:
    9215921
  • 财政年份:
    1992
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Standard Grant
Modeling Investment Uncertainty in the Costs of Global CO2 Emission Policy
对全球二氧化碳排放政策成本的投资不确定性进行建模
  • 批准号:
    9211937
  • 财政年份:
    1992
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Continuing Grant
International Conference on Stochastic Programming to be held at the University of Michigan in Ann Arbor, Michigan, August 13-18, 1989.
国际随机规划会议将于 1989 年 8 月 13 日至 18 日在密歇根州安娜堡市的密歇根大学举行。
  • 批准号:
    8820228
  • 财政年份:
    1989
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Standard Grant
Computational and Applied Aspects of Multiperiod Stochastic Programming
多周期随机规划的计算和应用方面
  • 批准号:
    8815101
  • 财政年份:
    1989
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Continuing Grant
Research Initiation: Computation and Approximation in Large-Scale Stochastic Linear Programs With Recourse
研究发起:大规模带追索随机线性规划的计算与逼近
  • 批准号:
    8304065
  • 财政年份:
    1983
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Standard Grant

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普林斯顿应用数学指南(The Princeton Companion to Applied Mathematics )的翻译与出版
  • 批准号:
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    2022
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    10.0 万元
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    数学天元基金项目

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