Joint optimization of order sequencing and rack scheduling in the robotic mobile fulfilment system

•We consider synchronous processing between orders and racks in the RMFS.•We find that jointly managing orders and racks can further improve efficiency.•The heuristic algorithm based on order similarity and rack suitability is proposed.•Our optimization halves the robotic tasks compared with benchma...

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Bibliographic Details
Published in:Computers & operations research 2021-11, Vol.135, p.105467, Article 105467
Main Authors: Yang, Xiying, Hua, Guowei, Hu, Linyuan, Cheng, T.C.E., Huang, Anqiang
Format: Article
Language:English
Subjects:
Integer programming
Linear programming
Mixed integer
Operations research
Optimization
Order picking
Order picking process
Order sequencing
Parts-to-picker
Rack scheduling
Racks
Robotic mobile fulfillment system
Robotics
Scheduling
Sequences
Task scheduling
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Summary:•We consider synchronous processing between orders and racks in the RMFS.•We find that jointly managing orders and racks can further improve efficiency.•The heuristic algorithm based on order similarity and rack suitability is proposed.•Our optimization halves the robotic tasks compared with benchmark solutions. 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.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2021.105467