TY - JOUR
T1 - Joint optimization of order sequencing and rack scheduling in the robotic mobile fulfilment system
AU - Yang, Xiying
AU - Hua, Guowei
AU - Hu, Linyuan
AU - Cheng, T. C.E.
AU - Huang, Anqiang
N1 - Funding Information:
This research was supported by the National Natural Science Foundation of China (NSFC) under grant number 71831001 and Beijing Logistics Informatics Research Base.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/11
Y1 - 2021/11
N2 - Putting the parts-to-picker idea into practice, the robotic mobile fulfillment system (RMFS) deploys robots to bring racks to stationary pickers so that the pickers focus only on order picking and the racks can be concurrently moved. An order sequencing and rack scheduling policy determines the processing sequence of given orders, as well as the allocation and arriving sequence of the racks. While order sequencing has been studied in the literature, there is little work on rack scheduling, which also affects the picking efficiency. We formulate a mixed-integer linear programming model and propose a two-stage solution procedure to address the problem of joint optimization of order sequencing and rack scheduling in the RMFS. Our numerical studies show that jointly optimizing order sequencing and rack scheduling reduces the robotic tasks by up to 59.8%, 50.8%, 32.0% compared with benchmark solutions, rack scheduling only, and order sequencing only, respectively. Our findings provide managerial insights that expanding the workbench capacity and storage density enhances processing performance.
AB - Putting the parts-to-picker idea into practice, the robotic mobile fulfillment system (RMFS) deploys robots to bring racks to stationary pickers so that the pickers focus only on order picking and the racks can be concurrently moved. An order sequencing and rack scheduling policy determines the processing sequence of given orders, as well as the allocation and arriving sequence of the racks. While order sequencing has been studied in the literature, there is little work on rack scheduling, which also affects the picking efficiency. We formulate a mixed-integer linear programming model and propose a two-stage solution procedure to address the problem of joint optimization of order sequencing and rack scheduling in the RMFS. Our numerical studies show that jointly optimizing order sequencing and rack scheduling reduces the robotic tasks by up to 59.8%, 50.8%, 32.0% compared with benchmark solutions, rack scheduling only, and order sequencing only, respectively. Our findings provide managerial insights that expanding the workbench capacity and storage density enhances processing performance.
KW - Order picking process
KW - Order sequencing
KW - Parts-to-picker
KW - Rack scheduling
KW - Robotic mobile fulfillment system
UR - http://www.scopus.com/inward/record.url?scp=85111327874&partnerID=8YFLogxK
U2 - 10.1016/j.cor.2021.105467
DO - 10.1016/j.cor.2021.105467
M3 - Journal article
AN - SCOPUS:85111327874
SN - 0305-0548
VL - 135
JO - Computers and Operations Research
JF - Computers and Operations Research
M1 - 105467
ER -