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Order Allocation Strategy Optimization in a Goods-to-Person Robotic Mobile Fulfillment System with Multiple Picking Stations

Gorde:
Argitaratua izan da:Applied Sciences vol. 15, no. 16 (2025), p. 9173-9202
Egile nagusia: Zhao Junpeng
Beste egile batzuk: Chu, Zhang
Argitaratua:
MDPI AG
Gaiak:
Turnover
Integer programming
Order picking
Mathematical models
Genetic algorithms
Optimization
Decision making
Robots
Design
Electronic commerce
Inventory control
Automation
Logistics
Strategic planning
Performance evaluation
Heuristic
Workloads
Inventory management
Inventory
Efficiency
Order processing
Robotics
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Laburpena:The order picking process in Goods-to-Person (G2P) systems involves a set of interdependent yet often separately addressed decisions, such as order allocation, sequencing, and rack handling. This study focuses on the joint optimization of order allocation, order sequencing, rack selection, and rack sequencing in a G2P robotic mobile fulfillment system with multiple picking stations. To model this complex problem, we develop a mathematical formulation and propose a two-phase heuristic algorithm that combines simulated annealing, genetic algorithms, and beam search for efficient solution. In addition, we explore and compare two order allocation strategies—order similarity and order association—across a range of operational scenarios. Extensive computational experiments and sensitivity analyses demonstrate the effectiveness of the proposed approach and provide insights into how strategic order allocation can significantly improve picking efficiency. Computational experiments on small-scale instances show that our algorithm achieves near-optimal solutions with up to 93.3% reduction in computation time compared to exact optimization for small cases. In large-scale scenarios, the order similarity strategy reduces rack movements by up to 44.8% and the order association strategy by up to 33.5% relative to a first-come, first-served baseline. Sensitivity analysis reveals that the association strategy performs best with fewer picking stations and lower rack capacity, whereas the similarity strategy is superior in systems with more stations or higher rack capacity. The findings offer practical guidance for the design and operation of intelligent warehousing systems.
ISSN:2076-3417
DOI:10.3390/app15169173
Baliabidea:Publicly Available Content Database