Optimization and Control for Systems in the Big-Data Era
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Optimization and Control for Systems in the Big-Data Era: Theory and Applications

Optimization and Control for Systems in the Big-Data Era: Theory and Applications

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About the Book

This book focuses on optimal control and systems engineering in the big data era. It examines the scientific innovations in optimization, control and resilience management that can be applied to further success. In both business operations and engineering applications, there are huge amounts of data that can overwhelm computing resources of large-scale systems. This “big data” provides new opportunities to improve decision making and addresses risk for individuals as well in organizations. While utilizing data smartly can enhance decision making, how to use and incorporate data into the decision making framework remains a challenging topic. Ultimately the chapters in this book present new models and frameworks to help overcome this obstacle. Optimization and Control for Systems in the Big-Data Era: Theory and Applications is divided into five parts. Part I offers reviews on optimization and control theories, and Part II examines the optimization and control applications. Part III provides novel insights and new findings in the area of financial optimization analysis. The chapters in Part IV deal with operations analysis, covering flow-shop operations and quick response systems. The book concludes with final remarks and a look to the future of big data related optimization and control problems.

Table of Contents:
Preface.- 1.        Optimization and Control for Systems in the Big-Data Era: An Introduction.- -          Tsan-Ming Choi, Jianjun Gao, James H. Lambert, Chi-Kong Ng, Jun Wang.- Part I: Reviews on Optimization and Control Theories.- 2.        Dual Control in Big Data Era: An Overview.- -          Peilin Fu.- 3.        Time Inconsistency and Self Control Optimization Problems: Progress and Challenges.- -          Yun Shi, Xiangyu Cui.- 4.        Quadratic Convex Reformulations for Integer and Mixed-Integer Quadratic Programs.- -          Baiyi Wu, Rujun Jiang  .- Part II: Reviews on Optimization and Control Applications.- 5.        Measurements of Financial Contagion: A Primary Review from the Perspective of Structural Break.- -          Xi Pei, Shushang Zhu  .- 6.        Asset-Liability Management in Continuous-Time: Cointegration and Exponential Utility.- -          Mei-Choi Chiu.- 7.        A Review of Modern Cryptography: From the World War II Era to the Big-Data Era.- -          Bojun Lu.- 8.        Supply Risk in the New Business Era: Supply Chain Competition and Cooperation.- -          Xiang Li, Yongjian Li, Linghua Zhao  .- Part III: Financial Optimization Analysis.- 9.        A Parameterized Method for Optimal Multi-period Mean-Variance Portfolio Selection with Liability.- -          Xun Li, Zhongfei Li, Xianping Wu, Haixiang Yao  .- 10.    Sparse and Multiple Risk Measures Approach for Data Driven Mean-CVaR Portfolio Optimization Model.- -          Jianjun Gao, Weiping Wu  .- 11.    Multistage Optioned Portfolio Selection: Mean-Variance Model and Target Tracking Model.- -          Jianfeng Liang  .- 12.    Multi-period portfolio selection with stochastic investment horizon.- -          Lan Yi<  .- Part IV: Operations Analysis.- 13.     A New Model and Method for Order Selection Problems in Flow-shop Production.- -          Jun Wang, Xiaoxiao Zhuang, Baiyi Wu  .- 14.    Quick Response Fashion Supply Chains in the Big Data Era .- -    Tsan-Ming Choi  .- Part V: Concluding Remarks.- 15.    Optimization and Control for Systems in the Big-Data Era: Concluding Remarks.- -          Tsan-Ming Choi, Jianjun Gao, James H. Lambert, Chi-Kong Ng, Jun Wang  


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Product Details
  • ISBN-13: 9783319851716
  • Publisher: Springer International Publishing AG
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: Theory and Applications
  • ISBN-10: 3319851713
  • Publisher Date: 28 Jul 2018
  • Height: 235 mm
  • No of Pages: 280
  • Returnable: Y
  • Width: 155 mm


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