The RFID-based real-time monitoring system and the management algorithm of RFIDs

Kuo, Chung‐Hao; Ting, Kuo-Cheng; Ying, Josh Jia-Ching; Chen, Shyr-Kuen; Chen, Yi-Chung

doi:10.1109/icmlc.2017.8108967

Cited by 2 publications

(1 citation statement)

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“…For instance, Kuo et al [4] [9] proposed methods that combine machine maintenance personnel experience, add-on triaxial accelerometers, and artificial neural networks for machinery fault detection and successfully applied these methods to spring factories. Kuo et al [5]successfully developed an RFID-based component management system that records what components a machine contains and what processing task it is performing, thereby greatly increasing component management efficiency. However, a comprehensive view of all relevant research revealed that only these two types of cost reduction techniques were discussed.…”

Section: Introductionmentioning

confidence: 99%

Multi-tasking multi-machine scheduling system for multi-stage multi-criteria production

et al. 2018

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In recent years, many major manufacturers have been incorporating Industry 4.0 technologies such as preventive fault detection, automated scheduling algorithms, and component management to increase productivity and reduce production costs. Achieving this objective requires a substantial amount of working capital to acquire large quantities of new machinery, equipment to extract data from the machinery, and high-priced big data analysis software. However, most factories in the world are small-or medium-sized companies and have not enough capital to replace their machinery or purchase big data analysis software. It is therefore almost impossible for these factories to reach the goal of Industry 4.0. Furthermore, most of the conventional automated production scheduling methods only consider a single criterion in scheduling, which is not applicable for actual situations. This study therefore proposed a multi-tasking multi-machine scheduling system for multi-stage multi-criteria production to address various shortcomings in existing methods. To achieve this goal, we proposed a novel concept based on skyline queries to assist in the scheduling process. Also, a data structure of "heap" is applied in this work to accelerate the scheduling process. The experimental results demonstrated the validity of the proposed approach.

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Section: Introductionmentioning

confidence: 99%