Work

An Attention-Based Learning Approach for Joint Optimization of Storage Selection and Order Picking Paths in Mobile Shelving Systems

Year: 2026

Type: article

Abstract: This research introduces an advanced attention-driven model designed to optimize mobile shelf warehouse order-picking. Our model incorporates an enhanced masking mechanism and context-aware decoder, streamlining the order-picking process. In essence, our model presents an attention model based heuristic solution to the long-standing problem of order-picking optimization, leveraging the latest in attention-based deep learning techniques. The attention model is combined with Apriori and the Adaptive Large Neighborhood Search (ALNS) algorithm to solve the bilevel combinatorial optimization model for mobile shelves. Compared to existing methods, our innovative model shows superior performance, offering significant potential in warehousing solutions.

Source: Mathematics

DOI: 10.3390/math14030559

Authors: Jiawei Zhang, Li Wang, Yu Chen, Dali Zhang, Sixiang Zhao

Institutions: Shanghai Jiao Tong University, State Grid Zhejiang Electric Power Company (China)

Language: English

Topic: Advanced Manufacturing and Logistics Optimization

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: State Grid Corporation of China