Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

被引:150
|
作者
Xu, Jie [1 ]
Huang, Edward [1 ]
Chen, Chun-Hung [1 ]
Lee, Loo Hay [2 ]
机构
[1] George Mason Univ, Dept Syst Engn & Operat Res, Fairfax, VA 22030 USA
[2] Natl Univ Singapore, Dept Ind & Syst Engn, Singapore 119077, Singapore
基金
美国国家科学基金会;
关键词
Simulation optimization; complex systems; big data analytics; cloud computing; systems engineering; ADAPTIVE SEARCH METHOD; CROSS-ENTROPY METHOD; BUDGET ALLOCATION; STOCHASTIC-APPROXIMATION; DISCRETE OPTIMIZATION; EMERGENCY-DEPARTMENT; ORDINAL OPTIMIZATION; ALGORITHM; SELECTION; DESIGN;
D O I
10.1142/S0217595915500190
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.
引用
收藏
页数:34
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