TY - GEN
T1 - Order Sequencing Problem in a Robotic Mobile Fulfillment System
AU - Li, Hanqi
AU - Zhang, Yaoxin
AU - Xiao, Zhiguo
AU - Li, Dongni
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With rapid development of Business-to-Customer (B2C) e-commerce, enormous goods assortment and fluctuated demand are presented in customer orders. Traditional manual warehouses have the demerits of low picking efficiency and high human cost, which misfit B2C e-commerce. To resolve the difficulties, a Robotic Mobile Fulfillment System (RMFS) is introduced and implemented. This paper studies the order sequencing problem in an RMFS with the situation that a rack can be reused among multiple picking stations in one rack schedule. In order to solve the problem, a Q-learning-based differential evolution algorithm is proposed. Compared with the existent algorithms, numerical experiments show that the proposed algorithm makes an evident improvement on order picking efficiency.
AB - With rapid development of Business-to-Customer (B2C) e-commerce, enormous goods assortment and fluctuated demand are presented in customer orders. Traditional manual warehouses have the demerits of low picking efficiency and high human cost, which misfit B2C e-commerce. To resolve the difficulties, a Robotic Mobile Fulfillment System (RMFS) is introduced and implemented. This paper studies the order sequencing problem in an RMFS with the situation that a rack can be reused among multiple picking stations in one rack schedule. In order to solve the problem, a Q-learning-based differential evolution algorithm is proposed. Compared with the existent algorithms, numerical experiments show that the proposed algorithm makes an evident improvement on order picking efficiency.
KW - Q-learning
KW - Robotic Mobile Fulfillment System
KW - differential evolution algorithm
KW - order sequencing
UR - http://www.scopus.com/inward/record.url?scp=85149534120&partnerID=8YFLogxK
U2 - 10.1109/CCDC55256.2022.10033985
DO - 10.1109/CCDC55256.2022.10033985
M3 - Conference contribution
AN - SCOPUS:85149534120
T3 - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
SP - 74
EP - 79
BT - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th Chinese Control and Decision Conference, CCDC 2022
Y2 - 15 August 2022 through 17 August 2022
ER -
