Graph Neural Networks and Deep Reinforcement Learning in Warehouse Order Picking and Batching - Literature Review

This paper is a systematic literature review on use of the Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) in warehouse. We first explore the use of DRL and GNN for optimization of order picking and batching in warehouse. Because of very little results on use of GNNs in optimization of order picking and batching we extended our search to general use of GNNs in warehouse environment. We identified different topics of research using Latent Dirichlet Allocation (LDA) and identified main problems in use of DRL and GNNs in warehouse environment.

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Publication Details

Published
2025-03-19
DOI
https://doi.org/10.18690/um.fov.2.2025.14
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
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article

Graph Neural Networks and Deep Reinforcement Learning in Warehouse Order Picking and Batching - Literature Review

Andrej Škraba, Nejc Čelik
Advanced Manufacturing and Logistics Optimization
article

Graph Neural Networks and Deep Reinforcement Learning in Warehouse Order Picking and Batching - Literature Review

Andrej Škraba, Nejc Čelik
article en

Abstract

This paper is a systematic literature review on use of the Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) in warehouse. We first explore the use of DRL and GNN for optimization of order picking and batching in warehouse. Because of very little results on use of GNNs in optimization of order picking and batching we extended our search to general use of GNNs in warehouse environment. We identified different topics of research using Latent Dirichlet Allocation (LDA) and identified main problems in use of DRL and GNNs in warehouse environment.

University of Maribor (SI)
Openalex Percentile: Top 94%
Advanced Manufacturing and Logistics Optimization
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