Bad-scenario-set Robust Optimization Framework With Two Objectives for Uncertain Scheduling Systems

Bing Wang; Xuedong Xia; Hexia Meng; Tao Li

Volume 4 Issue 1

Jan. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(1): 143-153

Bing Wang, Xuedong Xia, Hexia Meng and Tao Li, "Bad-scenario-set Robust Optimization Framework With Two Objectives for Uncertain Scheduling Systems," IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 143-153, Jan. 2017.

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Bing Wang, Xuedong Xia, Hexia Meng and Tao Li, "Bad-scenario-set Robust Optimization Framework With Two Objectives for Uncertain Scheduling Systems," IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 143-153, Jan. 2017.

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Bad-scenario-set Robust Optimization Framework With Two Objectives for Uncertain Scheduling Systems

School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

Funds:

National Natural Science Foundation of China 60874076

National Natural Science Foundation of China 61273182

National Natural Science Foundation of China 61273040

the Shanghai Rising-Star Program 15QA1402000

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Author Bio:
Xuedong Xia received his B. E. degree from the School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, China, in 2013. He is currently a master student at the School of Mechatronic Engineering and Automation, Shanghai University. His research interests include production planning and scheduling (e-mail: hytcxiaxdong@163.com)

Hexia Meng received her B. E. degree from the School of Electric Power, North China University of Water Resources and Electric Power, China, in 2012. She is currently a master student at the School of Mechatronic Engineering and Automation, Shanghai University. Her research interests include production planning and scheduling (e-mail: menghx2014@163.com)

Tao Li received the B. E. degree in automation from Nankai University, Tianjin, China, in 2004, and the Ph. D. degree in systems theory from the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences (CAS), Beijing, China, in 2009. From July 2009 to December 2013, he was a faculty member of AMSS, CAS. Since December 2013, he has been with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China, where now he is a professor. His current research interests include stochastic systems, networked control, multi-agent systems and sensor networks. He was mentioned as one of the five finalists for the Young Author Prize of the 17th IFAC World Congress, Seoul, 2008. He received the Special President Prize of Chinese Academy of Sciences in 2009, the Best Paper Award of the 7th Asian Control Conference with coauthors in 2009, the 2009 Singapore Millennium Foundation Research Fellowship and the 2010 Endeavor Research Fellowship from Australian Government. He was entitled Dongfang Distinguished Professor by Shanghai Municipality in 2012 and received National Science Found for Outstanding Young Scholars from National Natural Science Foundation of China in 2015. He is a member of IFAC Technical Committee on Networked Systems and a council member of Shanghai Association of Automation. He is a Senior Member of IEEE.(e-mail: sixumuzi@shu.edu.cn)
Corresponding author: Bing Wang received her B. E. degree in precision mechanism and precision instrument from University of Science and Technology of China in 1989, her Master degree in operations research and control theory from Shangdong University, China, in 2001, and her Ph. D. degree in control theory and control engineering from Shanghai Jiao Tong University, China, in 2005. From March 2008 to March 2009, she was a visiting scholar with the Department of Industry and System Engineering, University of Florida, USA. Before January 2011, she was a faculty member of Shandong University at Weihai, and currently she is a professor at the School of Mechatronic Engineering and Automation, Shanghai University, China. Bing Wang's current research interests include scheduling theory and algorithm, production planning and scheduling, robust discrete optimization theory and application, and health-care management and optimization. She is the author or coauthor of over 50 published papers. (e-mail: susanbwang@shu.edu.cn)
Received Date: 2015-01-23
Accepted Date: 2015-10-23

Abstract

Abstract

This paper proposes a robust optimization framework generally for scheduling systems subject to uncertain input data, which is described by discrete scenarios. The goal of robust optimization is to hedge against the risk of system performance degradation on a set of bad scenarios while maintaining an excellent expected system performance. The robustness is evaluated by a penalty function on the bad-scenario set. The bad-scenario set is identified for current solution by a threshold, which is restricted on a reasonable-value interval. The robust optimization framework is formulated by an optimization problem with two conflicting objectives. One objective is to minimize the reasonable value of threshold, and another is to minimize the measured penalty on the bad-scenario set. An approximate solution framework with two dependent stages is developed to surrogate the biobjective robust optimization problem. The approximation degree of the surrogate framework is analyzed. Finally, the proposed bad-scenario-set robust optimization framework is applied to a scenario job-shop scheduling system. An extensive computational experiment was conducted to demonstrate the effectiveness and the approximation degree of the framework. The computational results testified that the robust optimization framework can provide multiple selections of robust solutions for the decision maker. The robust scheduling framework studied in this paper can provide a unique paradigm for formulating and solving robust discrete optimization problems.
- Approximate solution,
- bad-scenario set,
- biobjective problem,
- job shop,
- robust optimization framework

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References(37)

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Bad-scenario-set Robust Optimization Framework With Two Objectives for Uncertain Scheduling Systems

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