TY - GEN
T1 - A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System
AU - Yan, Xiaohui
AU - Chan, Felix T.S.
AU - Zhang, Zhicong
AU - Lv, Cixing
AU - Li, Shuai
N1 - Funding Information:
Acknowledgements. This work is supported by the National Natural Science Foundation of China (Grant No. 61703102, 71971143, 71801045, 71801046), the National Key Research and Development Program of China (2018YFB1004004). The authors would like to thank The Hong Kong Polytechnic University Research Committee for financial and technical support.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/7/13
Y1 - 2020/7/13
N2 - A modified artificial bee colony algorithm is proposed for solving the scheduling optimization problem of multi-aisle automatic storage/retrieval system. The optimization model of the problem is analyzed and founded, in which the sequence constraint of tasks and calculation of the number of aisles are more realistic. According to the features of the problem, the encoding and decoding strategies for solutions to MABC algorithm are redesigned. Probability selection-based updating method is also introduced to enhance the neighborhood search and preserve the good fragments. The experimental results show that MABC can obtain better results than PSO and GA algorithm, and is a competitive approach for AS/RS scheduling optimization.
AB - A modified artificial bee colony algorithm is proposed for solving the scheduling optimization problem of multi-aisle automatic storage/retrieval system. The optimization model of the problem is analyzed and founded, in which the sequence constraint of tasks and calculation of the number of aisles are more realistic. According to the features of the problem, the encoding and decoding strategies for solutions to MABC algorithm are redesigned. Probability selection-based updating method is also introduced to enhance the neighborhood search and preserve the good fragments. The experimental results show that MABC can obtain better results than PSO and GA algorithm, and is a competitive approach for AS/RS scheduling optimization.
KW - Automatic storage retrieval system
KW - Modified artificial bee colony
KW - Scheduling optimization
UR - http://www.scopus.com/inward/record.url?scp=85088751731&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-53956-6_9
DO - 10.1007/978-3-030-53956-6_9
M3 - Conference article published in proceeding or book
AN - SCOPUS:85088751731
SN - 9783030539559
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 94
EP - 103
BT - Advances in Swarm Intelligence - 11th International Conference, ICSI 2020, Proceedings
A2 - Tan, Ying
A2 - Shi, Yuhui
A2 - Tuba, Milan
PB - Springer
T2 - 11th International Conference on Swarm Intelligence, ICSI 2020
Y2 - 14 July 2020 through 20 July 2020
ER -