Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises With Multi-Factory

The depth development and widespread application of edge intelligence technology based on the Internet of Things has led to edge-cloud collaboration and related research. In recent years, with the rapid development of the Internet of Things and the formation of super-city groups, the management characteristics of enterprises with multiple manufacturing plants served for headquarters have become increasingly obvious. The problem of order dynamic fluctuations caused by personalized customization requirements has become more prominent, which makes it impossible to do global long-period prediction or real-time short-period response relied solely on the cloud or edge. Therefore, this paper proposes a production system scheduling framework under the edge-cloud collaborative paradigm based on the dynamic fluctuation of orders under these background, and builds an edge-cloud collaborative scheduling model, which guarantees real-time distributed scheduling at the edge. It enabled the cloud to periodically predict the total completion time of production tasks at the headquarters based on the value-added data uploaded by the edge, and to support more accurate and efficient scheduling at the edge based on the prediction results. Finally, an example analysis proved the rationality of the scheduling mechanism and the effectiveness of the scheduling model. The proposed method can provide a certain reference for task scheduling in the edge-cloud collaborative production paradigm.

[1]  Hao Tan,et al.  A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems , 2018, J. Intell. Manuf..

[2]  Chai Xu-dong,et al.  Cloud manufacturing:a new service-oriented networked manufacturing model , 2010 .

[3]  Sai Ho Chung,et al.  Minimization of Order Tardiness Through Collaboration Strategy in Multifactory Production System , 2011, IEEE Systems Journal.

[4]  Fei Tao,et al.  Dynamic Supply-Demand Matching for Manufacturing Resource Services in Service-Oriented Manufacturing Systems: A Hypernetwork-Based Solution Framework , 2015 .

[5]  Fei Tao,et al.  Dynamic Supply-Demand Matching for Manufacturing Resource Services in Service-Oriented Manufacturing Systems: A Hypernetwork-Based Solution Framework , 2015 .

[6]  Maria Leonilde Rocha Varela,et al.  Technologies Integration for Distributed Manufacturing Scheduling in a Virtual Enterprise , 2011 .

[7]  Prem Prakash Jayaraman,et al.  Internet of Things and Edge Cloud Computing Roadmap for Manufacturing , 2016, IEEE Cloud Computing.

[8]  Lei Ren,et al.  An event-triggered dynamic scheduling method for randomly arriving tasks in cloud manufacturing , 2017, Int. J. Comput. Integr. Manuf..

[9]  Jin Wang,et al.  Game Theory Based Real‐Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing , 2017, Int. J. Intell. Syst..

[10]  Tung-Kuan Liu,et al.  Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms , 2017, J. Intell. Manuf..

[11]  István Mezgár,et al.  The challenge of networked enterprises for cloud computing interoperability , 2014, Comput. Ind..

[12]  V. Vinod,et al.  Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system , 2011 .

[13]  Lei Yin,et al.  A novel approach for product makespan prediction in production life cycle , 2015 .

[14]  Xavier Masip-Bruin,et al.  Managing resources continuity from the edge to the cloud: Architecture and performance , 2018, Future Gener. Comput. Syst..

[15]  Laurence T. Yang,et al.  A Cloud-Edge Computing Framework for Cyber-Physical-Social Services , 2017, IEEE Communications Magazine.

[16]  Jaewon Moon,et al.  A Heterogeneous IoT Data Analysis Framework with Collaboration of Edge-Cloud Computing: Focusing on Indoor PM10 and PM2.5 Status Prediction , 2019, Sensors.

[17]  Zhang Lin,et al.  Further discussion on cloud manufacturing , 2011 .

[18]  Toly Chen Embedding a back propagation network into fuzzy c-means for estimating job cycle time: wafer fabrication as an example , 2016, J. Ambient Intell. Humaniz. Comput..

[19]  Fei Tao,et al.  A Smart Manufacturing Service System Based on Edge Computing, Fog Computing, and Cloud Computing , 2019, IEEE Access.

[20]  Rong Chen,et al.  A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem , 2011, Int. J. Comput. Intell. Syst..

[21]  Lin Zhang,et al.  A Dynamic Task Scheduling Method Based on Simulation in Cloud Manufacturing , 2016 .

[22]  Essam Shehab,et al.  Uncertainties in Cloud Manufacturing , 2014, ISPE International Conference on Concurrent Engineering.

[23]  Pingyu Jiang,et al.  Deep neural networks based order completion time prediction by using real-time job shop RFID data , 2017, Journal of Intelligent Manufacturing.

[24]  Jun Dong,et al.  Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory , 2019, Applied Sciences.

[25]  Sai Ho Chung,et al.  An adaptive genetic algorithm with dominated genes for distributed scheduling problems , 2005, Expert Syst. Appl..

[26]  Lei Wang,et al.  Rescheduling strategy of cloud service based on shuffled frog leading algorithm and Nash equilibrium , 2018 .

[27]  Xiaofei Xu,et al.  Scheduling Methodology for Production Services in Cloud Manufacturing , 2012, 2012 International Joint Conference on Service Sciences.