Energy-Saving Operation of Multistage Stochastic Manufacturing Systems Based on Fuzzy Logic

Wang, J. F.; Fei, Zicheng; Chang, Qing; Fu, Yan; Li, S. Q.

doi:10.2507/ijsimm18(1)co1

Cited by 21 publications

(20 citation statements)

References 26 publications

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“…Compared with time-based data sampling in the fuzzy logic method [26], our event-driven method greatly eases the burden of data collection and conforms to the stochastic of the system. The dynamic updating of the ESW of the target machine during one round decision is more reasonable compared with the constant opportunity windows [27,29,[55][56][57] and will be illustrated in Section 5.…”

Section: Methodology Of Event-driven Online Energy Saving Decision Anmentioning

confidence: 99%

“…By formulating four ON-OFF control rules in serial production lines with Bernoulli machines, Jia et al [25] analyzed system performance measures in order to achieve high energy efficiency while maintaining high productivity. Wang et al [26] designed a fuzzy controller considering real-time data of buffers/machines in order to switch machine states and realize energy-efficient production.…”

Section: Energy-aware Manufacturing System Scheduling and Controlmentioning

confidence: 99%

“…Various models such as knowledge-based models, mathematical models, and discrete event simulation models are established for different decisions of energy-efficient manufacturing. The policies/rules [23][24][25] and fuzzy logic [26] are expert knowledge for the energy-saving operation of production systems. The big data analysis models [46], e.g., neural networks, rough set theory, and deep learning, can mine implicit relationships of production parameters considering energy cost and productivity.…”

Section: Model Viewmentioning

confidence: 99%

“…A typical serial manufacturing system including six machines and five buffers (6M5B) in References [26,29] is taken to illustrate the effectiveness of our method. This system is a simplified version of a real automotive production line.…”

Section: Simulation Case Of a Serial Manufacturing Systemmentioning

confidence: 99%

“…The electricity rate is assumed to be $0.2/kWh. It is assumed that no power is consumed in machine sleep mode for comparing performances with the methods in References [26,29]. Three scenarios are simulated as follows: baseline scenario with no energy saving decision and control (S1), only one machine being controlled for energy saving (S2), and multi-machine except the bottleneck machine being controlled for energy saving (S3).…”

Section: Simulation Case Of a Serial Manufacturing Systemmentioning

confidence: 99%

See 4 more Smart Citations

Event-Driven Online Machine State Decision for Energy-Efficient Manufacturing System Based on Digital Twin Using Max-Plus Algebra

et al. 2019

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Energy-efficient manufacturing is an important aspect of sustainable development in current society. The rapid development of sensing technologies can collect real-time production data from shop floors, which provides more opportunities for making energy saving decisions about manufacturing systems. In this paper, a digital twin-based bidirectional operation framework is proposed to realize energy-efficient manufacturing systems. The data view, model view, and service view of a digital twin manufacturing system are formulated to describe the physical systems in virtual space, to perform simulation analysis, to make decisions, and to control the physical systems for various energy-saving purposes. For online energy-saving decisions about machines in serial manufacturing systems, an event-driven estimation method of an energy-saving window based on Max-plus Algebra is presented to put the target machine to sleep, considering real-time production data of a system segment. A practical, simplified automotive production line is used to illustrate the effectiveness of the proposed method by simulation experiments. Our method has no restriction on machine failure mode and predefined parameters for energy-saving decision of machines. The proposed approach has potential use in synchronous and asynchronous manufacturing systems.

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