Real-time information driven intelligent navigation method of assembly station in unpaced lines

Zhang, Yingfeng; Xu, Jianxue; Sun, Shudong; Yang, Teng

doi:10.1016/j.cie.2014.09.007

Cited by 21 publications

(5 citation statements)

References 31 publications

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“…7 Based on the captured real-time information, the real-time information-driven intelligent navigation of assembly station in unpaced line, a real-time optimization scheduling for assigning shop floor material handling tasks, and IoT-enabled real-time production performance analysis and exception diagnosis model based on hierarchical timed colored Petri net are studied and built. [8][9][10] Zhong et al introduced a big data approach for mining the invaluable trajectory knowledge from enormous RFID-enabled logistics data. 11 CMfg is widely introduced into the current manufacturing field and integrated with IoT technology to achieve the integration, management, and sharing of manufacturing in formation and resources.…”

Section: Scheduling Methods For Cmfgmentioning

confidence: 99%

“…After capturing the dynamic information of manufacturing resources, instead of directly entering optimization stage, some adaptive controlling mechanisms are entailed . Based on the captured real‐time information, the real‐time information‐driven intelligent navigation of assembly station in unpaced line, a real‐time optimization scheduling for assigning shop floor material handling tasks, and IoT‐enabled real‐time production performance analysis and exception diagnosis model based on hierarchical timed colored Petri net are studied and built . Zhong et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(1) If the output of Algorithm 1 is empty, compute nt k=1 U h k (s). (2) Obtain the ACNE solution according to (8). If there are more than one ACNE solution, randomly select one.…”

Section: Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

Zhang

Wang

Liu

et al. 2016

Int. J. Intell. Syst.

Self Cite

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With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real‐time, accurate, and value‐added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real‐time data. To achieve the real‐time data‐driven optimization decision, a dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real‐time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real‐time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and optimization strategy for process tasks are designed and developed to implement the dynamic optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real‐time scheduling for four kinds of exceptions is also discussed.

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Section: Scheduling Methods For Cmfgmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

Zhang

Wang

Liu

et al. 2016

Int. J. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In terms of demand, we observe a transition from repetitive orders, mass production mode with stable production processes to customized demands, small-batch multi-variety production mode with highly dynamic production processes (Sim and Rogers 2008;Fogliatt et al 2012;Deshmukh et al 2017). In terms of technology available to help fulfill this demand, production execution is transitioning from manual operations to automatic and even intelligent operations, the production data is transitioning from closed and backward to real-time and transparent data, and production planning is transitioning from prior-plan mode to adaptive decision making and online control (Powell and Skjelstad 2012;Olivella et al 2008;Zhang et al 2015;Marodin and Saurin 2013). The emergence of these new backgrounds motivates us to unify LP through re-defining the control logics of traditional LP methods using a process control perspective, e.g.…”

Section: Introductionmentioning

confidence: 99%

IoT-enabled dynamic lean control mechanism for typical production systems

Zhang

Zhou

et al. 2018

J Ambient Intell Human Comput

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The emergence and subsequent popularization of lean has been one of the most significant developments in the history of operations management. However, there is a lack of systematic theory on the control framework underlying lean production. It is therefore difficult to conduct more in-depth research on Lean theory, specifically in the context of emerging technologies as smart manufacturing or Industry 4.0. In this study, process control theory is used to redefine several major lean methods and tools. Then a Lean-Oriented Optimum-State Control Theory (L-OSCT) is proposed that integrates these lean methods and tools into optimum-state control theory. On the level of method and mechanism, we adopt a recently emerged synchronization approach to obtain global-wide leanness of a large-scale system. L-OSCT provides dynamic process control in industrial networking systems. At last, a case study in a large-size paint making company in China is used to validate the effectiveness of the approach.

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“…Nowadays, there are trends to use real time data [4][5][6]. One of such areas is the manual assembly, which has always been and will probably remain present in the engineering industry.…”

mentioning

confidence: 99%

Design of on-line system for measuring and tracking time of assembly

2016

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Abstract:Manual assembly performed at assembly workstations nowadays still has a unique place in different kinds of production. To increase the productivity and quality of manual assembly it is necessary to analyse the existing workplaces and find ways to improve and streamline work done at these workplaces. The article deals with the design of a model for on-line analysis of a manual assembly process. The proposed model is based on the use of sensors or the so-called button-box and the use of software for recording and evaluating data. Based on the obtained data it is then possible to evaluate the time characteristics of the assembly process, as well as to find sources of delays and mistakes and then take appropriate action to correct them.

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Real-time information driven intelligent navigation method of assembly station in unpaced lines

Cited by 21 publications

References 31 publications

Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

IoT-enabled dynamic lean control mechanism for typical production systems

Design of on-line system for measuring and tracking time of assembly

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