TY - JOUR
T1 - A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing
AU - Zhong, Ray Y.
AU - Huang, George Q.
AU - Lan, Shulin
AU - Dai, Q. Y.
AU - Zhang, T.
AU - Xu, Chen
N1 - Funding Information:
This research is supported by HKU Small Project Funding ( 201309176013 ) and National Natural Science Foundation of China (Grant No. 51405307 ). Acknowledgements are also given to Zhejiang Provincial, Hangzhou Municipal and Lin’an City Governments, 2009 Guangdong Modern Information Service Fund ( GDIID2009IS048 ), and Guangdong technology service plan fund (2013B040403005). Special thanks are given to Huaiji Dengyun Auto-parts (Holding) Co., Ltd., Huaiji, Zhaoqing, Guangdong, China.
Publisher Copyright:
© 2015 Elsevier Ltd. All rights reserved.
PY - 2015/10
Y1 - 2015/10
N2 - Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.
AB - Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.
KW - Advanced production planning and scheduling (APPS)
KW - Radio frequency identification (RFID)
KW - Real-time
KW - Shop floor
KW - Two-level
KW - Ubiquitous manufacturing
UR - http://www.scopus.com/inward/record.url?scp=84960495265&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2015.01.002
DO - 10.1016/j.aei.2015.01.002
M3 - Journal article
AN - SCOPUS:84960495265
SN - 1474-0346
VL - 29
SP - 799
EP - 812
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
IS - 4
ER -