Order Sequencing Problem in a Robotic Mobile Fulfillment System

Hanqi Li; Yaoxin Zhang; Zhiguo Xiao; Dongni Li

doi:10.1109/CCDC55256.2022.10033985

Order Sequencing Problem in a Robotic Mobile Fulfillment System

Hanqi Li, Yaoxin Zhang, Zhiguo Xiao^*, Dongni Li

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	74-79
Number of pages	6
ISBN (Electronic)	9781665478960
DOIs	https://doi.org/10.1109/CCDC55256.2022.10033985
Publication status	Published - 2022
Event	34th Chinese Control and Decision Conference, CCDC 2022 - Hefei, China Duration: 15 Aug 2022 → 17 Aug 2022

Publication series

Name	Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022

Conference

Conference	34th Chinese Control and Decision Conference, CCDC 2022
Country/Territory	China
City	Hefei
Period	15/08/22 → 17/08/22

Keywords

Q-learning
Robotic Mobile Fulfillment System
differential evolution algorithm
order sequencing

Access to Document

10.1109/CCDC55256.2022.10033985

Cite this

Li, H., Zhang, Y., Xiao, Z., & Li, D. (2022). Order Sequencing Problem in a Robotic Mobile Fulfillment System. In Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022 (pp. 74-79). (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC55256.2022.10033985

@inproceedings{a976e28b92e545ce836383c70a52274f,

title = "Order Sequencing Problem in a Robotic Mobile Fulfillment System",

abstract = "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.",

keywords = "Q-learning, Robotic Mobile Fulfillment System, differential evolution algorithm, order sequencing",

author = "Hanqi Li and Yaoxin Zhang and Zhiguo Xiao and Dongni Li",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 34th Chinese Control and Decision Conference, CCDC 2022 ; Conference date: 15-08-2022 Through 17-08-2022",

year = "2022",

doi = "10.1109/CCDC55256.2022.10033985",

language = "English",

series = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "74--79",

booktitle = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",

address = "United States",

}

Li, H, Zhang, Y, Xiao, Z & Li, D 2022, Order Sequencing Problem in a Robotic Mobile Fulfillment System. in Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 74-79, 34th Chinese Control and Decision Conference, CCDC 2022, Hefei, China, 15/08/22. https://doi.org/10.1109/CCDC55256.2022.10033985

Order Sequencing Problem in a Robotic Mobile Fulfillment System. / Li, Hanqi; Zhang, Yaoxin; Xiao, Zhiguo et al.
Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 74-79 (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Order Sequencing Problem in a Robotic Mobile Fulfillment System

AU - Li, Hanqi

AU - Zhang, Yaoxin

AU - Xiao, Zhiguo

AU - Li, Dongni

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 -

Order Sequencing Problem in a Robotic Mobile Fulfillment System

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this