Optimization and Control for Systems in the Big-Data Era

Tsan-Ming Choi (Jason) is currently Professor of Fashion Business at The Hong Kong Polytechnic University. Over the past ten years, he has actively participated in a variety of research projects on supply chain management and applied optimization for fashion business operations. He has authored/edited fifteen research handbooks. He has published extensively in leading journals such as Annals of Operations Research, Automatica, Decision Support Systems, European Journal of Operational Research, IEEE Transactions on Automatic Control, IEEE Transactions on Engineering Management, IEEE Transactions on Industrial Informatics, Naval Research Logistics, Production and Operations Management, Service Science, Transportation Research - Part E, etc. He has consulted several listed companies on supply chain management related topics. He is currently a senior editor of Production and Operations Management, an associate editor of Asia-Pacific Journal of Operational Research, DecisionSupport Systems, IEEE Transactions on Systems, Man and Cybernetics - Systems, Information Sciences, International Transactions in Operational Research, and Transportation Research - Part E. In the past, he served as an executive committee member/officer of professional organizations such as IEEE-SMC(HK) and POMS(HK). He is the recipient of the President's Award for Excellent Achievements of The Hong Kong Polytechnic University. He received the Best Associate Editor Award of IEEE Systems, Man, and Cybernetics Society in two consecutive years (2013 and 2014).

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.

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