Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing

Zhao-Hui Liu; Zhong-Jie Wang; Chen Yang

doi:10.1007/s40436-019-00281-2

Advances in Manufacturing ›› 2019, Vol. 7 ›› Issue (4) : 374 -388. DOI: 10.1007/s40436-019-00281-2

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Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing

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¹ College of Electronics and Information Engineering, Tongji University, 201804, Shanghai, People’s Republic of China

² College of Engineering Science and Technology, Shanghai Ocean University, 201306, Shanghai, People’s Republic of China

^b +86-13764400275 wang_zhongjie@tongji.edu.cn

Zhao-Hui Liu is a PhD student in control science and engineering of Tongji University, China. He received the M.Eng. degree in guidance, navigation and control from Northeastern University, China in 2014. His research interests primarily include game theory, evolutionary algorithms, networked manufacturing, optimization resource scheduling, mechanism design, multi-agent system, complex system and pinning control.

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Zhong-Jie Wang received the Ph.D. degree in Control Theory and Control Engineering from Tongji University, Shanghai, China, in 2000. From 2000 to 2002, she was a Post-Doctoral Program Fellow with Control Science & Engineering of Tongji University, China. She joined the Department of Control Science and Engineering of Tongji University at 2002. She was an Associate Professor From 2002 to 2006 and a professor from 2006 up to now. She has authored or co-authored over 80 articles in refereed international journals and conferences. Her research interests involve smart manufacturing, data analysis, intelligent decision and AI application. She is an IEEE Member.

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Chen Yang is currently an asistant professor of college of engineering science and technology in Shanghai Ocean University. She received the M.Eng. degree in control theory and control engineering from Chongqing University, China in 2004, and the Ph.D. degree in control theory and control engingeering from Tongji University, China in 2017. Her research interests are primarily in Internet of things of agriculture, cloud manufacturing and application of aquatic product quality traceability technology.

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Abstract

Cloud manufacturing is a new kind of networked manufacturing model. In this model, manufacturing resources are organized and used on demand as market-oriented services. These services are highly uncertain and focus on users. The information between service demanders and service providers is usually incomplete. These challenges make the resource scheduling more difficult. In this study, an iterative double auction mechanism is proposed based on game theory to balance the individual benefits. Resource demanders and providers act as buyers and sellers in the auction. Resource demanders offer a price according to the budget, the delivery time, preference, and the process of auction. Meanwhile, resource providers ask for a price according to the cost, maximum expected profit, optimal reservation price, and the process of auction. A honest quotation strategy is dominant for a participant in the auction. The mechanism is capable of guaranteeing the economic benefits among different participants in the market with incomplete information. Furthermore, the mechanism is helpful for preventing harmful market behaviors such as speculation, cheating, etc. Based on the iterative double auction mechanism, manufacturing resources are optimally allocated to users with consideration of multiple objectives. The auction mechanism is also incentive compatibility.

Keywords

Cloud manufacturing / Resource scheduling / Multi-objective optimization / Iterative double auction / Incentive compatibility

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Zhao-Hui Liu, Zhong-Jie Wang, Chen Yang. Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing. Advances in Manufacturing, 2019, 7(4): 374-388 DOI:10.1007/s40436-019-00281-2

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