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CAREER: A Study of Stochastic and Robust Integer Programming: Algorithms, Computations and Applications

CAREER: A Study of Stochastic and Robust Integer Programming: Algorithms, Computations and Applications
职业:随机和鲁棒整数规划研究:算法、计算和应用
批准号:
0942156
负责人:
Yongpei Guan
金额:
$40.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-05-17 至 2014-07-31

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中文摘要
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英文摘要
This Faculty Early Career Development (CAREER) award provides funds for research and education activities to advance the theory, computations and applications of stochastic and robust integer programming. Integer decision variables are commonly used in models for decision making problems. Moreover, in real-time decision making problems, instance parameters are usually uncertain. This work will study stochastic and robust integer programming approaches to formulate and solve decision making problems with integer decision variables and uncertain parameters. The research activities include 1) studying polyhedral structures and polynomial time algorithms for fundamental problem formulations, 2) discovering new methods to integrate cutting planes with decomposition algorithms, 3) exploring cyberinfrastructure to implement the algorithms efficiently in high performance computing facilities, and 4) integrating theoretical analysis with application problems in power asset optimization, joint pricing and inventory planning, and weather forecasting and evacuation plans. The education activities include 1) promoting the practice of stochastic and robust integer programming as an efficient approach to solve decision making under uncertainty problems and 2) stimulating student interest in research in science and technology, especially in operations research. The research and education tasks are integrated in a way that both benefit from each other. If successful, the results of the research activities will lead to methodology innovations for solving large-scale decision making under uncertainty problems and advance the utilization of cyberinfrastructure in optimization. These research outcomes will provide new methodologies that can be used to instruct the next generation of engineers on efficient optimization methods. In addition, the activities will have big impact on industry and society by disseminating advanced methodologies to practitioners and providing outreach to underrepresented minority students, through collaborations with local industry and research centers such as Center for Engineering Logistics and Distribution, Center for Analysis and Prediction of Storms, and the K20 Center.
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