TY - GEN
T1 - Cloud-based cyber-physical robotic mobile fulfillment systems considering order correlation pattern
AU - Keung, K. L.
AU - Lee, C. K.M.
AU - Ji, P.
AU - Huo, Jiage
N1 - Funding Information:
This work was supported by the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong (RK2F). Our gratitude is also extended to the Research Committee and the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong and The Innovation and Technology Commission, The Government of the Hong Kong SAR, Hong Kong for support of this project (PRP/002/19FX/K.ZM31).
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - Ordering picking is the most time- and cost-consuming operation in the Robotic Mobile Fulfillment System (RMFS) and affects the entire supply chain operation efficiency and effectiveness. With the aid of digital operations, Cyber-Physical Systems (CPS) provide a nearly real-time control and response in the virtualized environment, thereby conducting a virtual prototype for near real-time simulation and prediction. The research presented in this paper explores the application of CPS in RMFS, considering the order correlation pattern. Four algorithms: Apriori algorithm, Frequent Pattern Growth algorithm, ECLAT algorithm and k-modes algorithm are introduced to reduce robotic conflicts of robots and enhance the capacity management in RMFS. The total completion time based on frequent itemset assignment is less than that based on random storage assignment. However, the dock grid conflicts are increased because the most frequent items are concentrated in a particular area.
AB - Ordering picking is the most time- and cost-consuming operation in the Robotic Mobile Fulfillment System (RMFS) and affects the entire supply chain operation efficiency and effectiveness. With the aid of digital operations, Cyber-Physical Systems (CPS) provide a nearly real-time control and response in the virtualized environment, thereby conducting a virtual prototype for near real-time simulation and prediction. The research presented in this paper explores the application of CPS in RMFS, considering the order correlation pattern. Four algorithms: Apriori algorithm, Frequent Pattern Growth algorithm, ECLAT algorithm and k-modes algorithm are introduced to reduce robotic conflicts of robots and enhance the capacity management in RMFS. The total completion time based on frequent itemset assignment is less than that based on random storage assignment. However, the dock grid conflicts are increased because the most frequent items are concentrated in a particular area.
KW - Cyber-Physical System
KW - Internet of Things
KW - Order Correlation Pattern
KW - Robotic Mobile Fulfillment Systems
UR - http://www.scopus.com/inward/record.url?scp=85099737165&partnerID=8YFLogxK
U2 - 10.1109/IEEM45057.2020.9309904
DO - 10.1109/IEEM45057.2020.9309904
M3 - Conference article published in proceeding or book
AN - SCOPUS:85099737165
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 113
EP - 117
BT - 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
PB - IEEE Computer Society
T2 - 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
Y2 - 14 December 2020 through 17 December 2020
ER -