11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

Research Article

A Scheduling Method for IOT-aided Packaging and Printing Manufacturing System

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  • @INPROCEEDINGS{10.4108/eai.19-8-2015.2260437,
        author={wenxiang li and Chunchun Pi and Mei Han and Chong Ran and Wei Chen and Peng Ke},
        title={A Scheduling Method for IOT-aided Packaging and Printing Manufacturing System},
        proceedings={11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness},
        publisher={IEEE},
        proceedings_a={QSHINE},
        year={2015},
        month={9},
        keywords={package manufacturing; internet of things scheduling genetic algorithm},
        doi={10.4108/eai.19-8-2015.2260437}
    }
    
  • wenxiang li
    Chunchun Pi
    Mei Han
    Chong Ran
    Wei Chen
    Peng Ke
    Year: 2015
    A Scheduling Method for IOT-aided Packaging and Printing Manufacturing System
    QSHINE
    IEEE
    DOI: 10.4108/eai.19-8-2015.2260437
wenxiang li1,*, Chunchun Pi1, Mei Han2, Chong Ran1, Wei Chen3, Peng Ke1
  • 1: Wuhan University of Science and Technology
  • 2: General Hotstrip Rolling Mill of WISCO
  • 3: China University of Mining and Technology
*Contact email: liwx2006@hotmail.com

Abstract

To meet the demand of effective control of production in packaging and printing industry, this paper proposes a manufacturing-assist system based on Internet of Things (IOT) techniques. The system is composed of reliable network connection with wireless mesh networks and widely deployed sensor nodes. With smart sensing, transmission and processing for the states of manufacturing facilities, products and production procedures, the system can exert efficient surveillance and control over the manufacturing procedure. Based on this system, this paper further designs a method for scheduling subtasks both among facilities and inside each facility. The method is implemented by Genetic Algorithm for optimization objectives such as minimizing overall production delay and minimizing overall production cost. Simulation and on-spot experiment in enterprise showed the superiority of the method for the optimization objectives.