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Conference Paper: An Analytic Approach for Workers’ Fatigue Examination Using RFID-Enabled Production Data
Title | An Analytic Approach for Workers’ Fatigue Examination Using RFID-Enabled Production Data |
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Authors | |
Keywords | Data-driven approach Fatigue trajectory Radio frequency identification (RFID) |
Issue Date | 2021 |
Publisher | Springer. |
Citation | The proceedings of the 10th International Conference on Logistics, Informatics and Service Sciences (LISS 2020), Beijing, China, 25–28 July 2020, p. 119-132 How to Cite? |
Abstract | With the advantages of long-distance contactless identification and data storage capacity, the use of radio frequency identification (RFID) technology in the fields of manufacturing, transportation and logistics has been widely reported. Fatigue of workers plays a critical role in impacting the manufacturing efficiency because it reduces productivity and increases accident rates. Therefore, the workers’ fatigue must be well examined and addressed. This paper thus proposes an analytic approach to use RFID captured production data and builds an effective method for mining the structural insight to predict the fatigue trajectory in workplace from a huge number of RFID data which may be full of inaccurate, incomplete and missing records. In this research, realistic processing time is used to measure the workers’ fatigue. Based on a general framework for the fatigue examination, the proposed approach is able to estimate the employees’ fatigue trajectory within designated period of time using RFID-enabled production data. Different genders and shifts are considered to find the key impact factors on fatigue. |
Persistent Identifier | http://hdl.handle.net/10722/299058 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Yang, Y | - |
dc.contributor.author | Zhong, R | - |
dc.date.accessioned | 2021-04-28T02:25:38Z | - |
dc.date.available | 2021-04-28T02:25:38Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | The proceedings of the 10th International Conference on Logistics, Informatics and Service Sciences (LISS 2020), Beijing, China, 25–28 July 2020, p. 119-132 | - |
dc.identifier.isbn | 9789813343580 | - |
dc.identifier.uri | http://hdl.handle.net/10722/299058 | - |
dc.description.abstract | With the advantages of long-distance contactless identification and data storage capacity, the use of radio frequency identification (RFID) technology in the fields of manufacturing, transportation and logistics has been widely reported. Fatigue of workers plays a critical role in impacting the manufacturing efficiency because it reduces productivity and increases accident rates. Therefore, the workers’ fatigue must be well examined and addressed. This paper thus proposes an analytic approach to use RFID captured production data and builds an effective method for mining the structural insight to predict the fatigue trajectory in workplace from a huge number of RFID data which may be full of inaccurate, incomplete and missing records. In this research, realistic processing time is used to measure the workers’ fatigue. Based on a general framework for the fatigue examination, the proposed approach is able to estimate the employees’ fatigue trajectory within designated period of time using RFID-enabled production data. Different genders and shifts are considered to find the key impact factors on fatigue. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | LISS 2020: Proceedings of the 10th International Conference on Logistics, Informatics and Service Sciences | - |
dc.subject | Data-driven approach | - |
dc.subject | Fatigue trajectory | - |
dc.subject | Radio frequency identification (RFID) | - |
dc.title | An Analytic Approach for Workers’ Fatigue Examination Using RFID-Enabled Production Data | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Zhong, R: zhongzry@hku.hk | - |
dc.identifier.authority | Zhong, R=rp02116 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-981-33-4359-7_9 | - |
dc.identifier.hkuros | 322237 | - |
dc.identifier.spage | 119 | - |
dc.identifier.epage | 132 | - |
dc.publisher.place | Singapore | - |