Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning

Puiseau, Constantin Waubert de; Nanfack, Dimitri Tegomo; Tercan, Hasan; Löbbert-Plattfaut, Johannes; Meisen, Tobias

doi:10.3390/technologies10060129

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Otra publicación demuestra cómo las técnicas de aprendizaje por refuerzo profundo, como el DRL, pueden aplicarse con éxito en la industria del almacenamiento para abordar problemas complejos de asignación de ubicaciones y optimización de costos (Yang et al, 2021). Los José David Giraldo Castellanos, Paola Marcela Álzate Montoya, Jonathan Andrés Arias Bohórquez Prácticas de optimización de la cadena de suministro: mapeo de literatura datos generados por los sistemas de gestión de almacenes pueden tener un uso más amplio que simplemente la trazabilidad, ya que pueden contribuir a la mejora del diseño estratégico de los sistemas de almacenamiento (Waubert de Puiseau et al, 2022). Tufano et al, (2022) proponen un sistema de soporte a la toma de decisiones para abordar la asignación de ubicación de almacenamiento en entornos multicliente, con un enfoque en la mejora de la eficiencia operativa, particularmente en la preparación de pedidos y la asignación de espacio en el almacén.…”

Section: Modelos De Planificación De Almacenesunclassified

Prácticas de optimización de la cadena de suministro: mapeo de literatura

Giraldo Castellanos,

Álzate Montoya,

Arias Bohórquez

2024

Publ. investig.

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The evolution of logistics operations has been viewed from different perspectives, starting with its initially heuristic approach and transforming into a set of advanced regulations and practices. The main objective of the research is to analyze the most influential publications, authors, institutions, and nations in the field of logistics operations, explore current research trends, and analyze the knowledge evolution in this area. To achieve this, a systematic literature review of Scopus data from 2002 to 2022 was conducted, with a focus on organizing, observing, and systematizing data. The results revealed that research on Cross-Docking, order picking, and warehouse planning emphasizes the need to optimize logistic processes to enhance efficiency, reduce costs, and maintain competitiveness in an ever-changing business environment. These studies reflect the ongoing quest for effective solutions to address current logistical challenges and underscore the importance of innovation and optimization in supply chain and logistics management.

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Section: Modelos De Planificación De Almacenesunclassified

Prácticas de optimización de la cadena de suministro: mapeo de literatura

Giraldo Castellanos,

Álzate Montoya,

Arias Bohórquez

2024

Publ. investig.

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“…By analyzing past order data, these methods anticipate future demand patterns, thereby reducing the time and resources needed for storage location adjustments, these dynamic methods, utilizes sophisticated algorithms that, while effective, come with a trade-off in terms of time and computational resource [11]. [16].…”

Section: A Storage Location Assignmentmentioning

confidence: 99%

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

Kalkha,

Khiat,

Bahnasse

et al. 2024

IEEE Access

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In the rapidly evolving domain of e-commerce, effective warehouse management emerges as a critical factor for ensuring timely deliveries. This paper addresses the Storage Location Assignment Problem (SLAP) in e-commerce warehouses, a challenge intensified by varying product volumes and unpredictable demands. We introduce a novel Intelligent Storage Location Assignment (ISLA) method that utilizes advanced time series clustering algorithms specifically, Self-Organizing maps, dynamic time warping-Based k-means, and Agglomerative Hierarchical Clustering (AHC), to optimize order fulfillment and enhance warehouse efficiency. By clustering and positioning items with similar demand patterns, our approach minimizes order preparation time, reduces unnecessary warehouse movements, and improves operational flows. Our empirical evaluation, based on a real-world dataset from Kaggle, demonstrates the superiority of AHC in efficiently grouping high-turnover items, as evidenced by higher silhouette scores. Applying this method in simulations across various picking strategies such as s-shape, mid-point, discrete order picking, zone picking, and batch picking, we achieve significant efficiency improvements. Notably, our ISLA method results in up to 61% and 69% efficiency gains under s-shape and midpoint routing policies, respectively, outperforming traditional random and ABC storage assignments. These results not only highlight the significant potential of Artificial Intelligence (AI) in revolutionizing warehouse operations but also bridge the existing knowledge gap by showcasing a practical and impactful application of AI in SLAP. Our research advances the field of smart logistics, emphasizing the critical role of AI-driven intelligent storage location assignment in optimizing warehouse processes and enhancing the efficiency of the e-commerce supply chain.

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“…A major factor in the efficient operation of warehouses is the strategic storage location assignment of arriving goods, termed the dynamic storage location assignment problem (DSLAP). The eleventh paper [11] presented a real-world case of the DSLAP, in which deep reinforcement learning (DRL) is used to derive a suitable storage location assignment strategy to decrease transportation costs within the warehouse. The DRL agent is trained on historic data of storage and retrieval operations gathered over one year of operation.…”

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confidence: 99%