TY - GEN
T1 - A Shortest Path Graph Attention Network and Non-traditional Multi-deep Layouts in Robotic Mobile Fulfillment System
AU - Keung, K. L.
AU - Xia, Liqiao
AU - Lee, C. K.M.
AU - Leung, C. Y.
N1 - Funding Information:
ACKNOWLEDGMENT:
This work was supported by the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong and the Laboratory for Artificial Intelligence in Design (Project Code: RP2-2). Our gratitude is also extended to the Research Committee and the Department of Industrial and Systems Engineering (RK2F), The Hong Kong Polytechnic University, Hong Kong. The authors would like to express their appreciation to the anonymous case company for their assistance with the data collection.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/12
Y1 - 2022/12
N2 - The rapid development of E-commerce has forced warehouse operations to develop towards a robotics-based system named Robotic Mobile Fulfillment System (RMFS), in which shortest path planning and conflict recognition play a vital role in enhancing the operational efficiency under multiple mobile robots movement. Compared to the traditional double-deep layout in Automatic Guided Vehicle (AGV) system, this paper proposes multi-deep based layouts in RMFS, including the modification of Flying-V, Fishbone and Chevron layouts. Under these circumstances, this paper further adopts the shortest path graph attention network in RMFS. This paper considers the Dijkstra algorithm as a baseline and compares it with Biased Cost Pathfinding methods, Anytime Repairing A-star and Flow-Annotation Re-planning methods. The shortest path graph attention network adoption in RMFS should enhance the overall operational efficiency and effectiveness under different layouts scenarios with different path planning methods.
AB - The rapid development of E-commerce has forced warehouse operations to develop towards a robotics-based system named Robotic Mobile Fulfillment System (RMFS), in which shortest path planning and conflict recognition play a vital role in enhancing the operational efficiency under multiple mobile robots movement. Compared to the traditional double-deep layout in Automatic Guided Vehicle (AGV) system, this paper proposes multi-deep based layouts in RMFS, including the modification of Flying-V, Fishbone and Chevron layouts. Under these circumstances, this paper further adopts the shortest path graph attention network in RMFS. This paper considers the Dijkstra algorithm as a baseline and compares it with Biased Cost Pathfinding methods, Anytime Repairing A-star and Flow-Annotation Re-planning methods. The shortest path graph attention network adoption in RMFS should enhance the overall operational efficiency and effectiveness under different layouts scenarios with different path planning methods.
KW - Graph Attention Network
KW - Robotic Mobile Fulfillment System
KW - Shortest Path
KW - Warehouse Layout
UR - https://www.scopus.com/pages/publications/85146350380
U2 - 10.1109/IEEM55944.2022.9989607
DO - 10.1109/IEEM55944.2022.9989607
M3 - Conference article published in proceeding or book
AN - SCOPUS:85146350380
SN - 9781665486880
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 655
EP - 659
BT - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
PB - IEEE Computer Society
T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
Y2 - 7 December 2022 through 10 December 2022
ER -