The recent growth in e-commerce during the COVID-19 pandemic has intensified the demand for efficient warehouses with high storage density and throughput. Puzzle-based storage solutions have demonstrated their effectiveness in optimizing storage capacity with limited spaces. To further enhance these capabilities, the Logistics 4.0 Lab at NTNU and Wheel.me have introduced a new system that utilizes autonomous wheels, enabling storage racks to move in any direction. This innovative approach holds the potential to significantly improve throughput capacity. Existing studies have primarily focused on evaluating retrieval performance based on factors like escort locations, I/O points, and movement constraints. This paper aims to enhance retrieval time by implementing a two-class-based storage policy. By strategically placing high-turnover items near the I/O point, travel distances are minimized, resulting in faster retrieval. To evaluate the effectiveness of the class-based storage system, various input parameters are considered, including the ABC curve, system size, class A shape, and shape ratio. The objective is to examine how the optimized system with a class-based storage policy, impacts the performance of the new configuration. The findings reveal significant reductions in cycle time, with improvements of up to 150% compared to random storage, depending on the specific system configuration and characteristics of the stored items.
La recente crescita dell’e-commerce durante la pandemia da COVID-19 ha evidenziato la necessità di una gestione efficiente delle operazioni di magazzino, insieme a prestazioni elevate in termini di densità e tempestività. Recenti studi sui puzzle-based storage systems (PBS) hanno evidenziato il loro potenziale nel massimizzare la capacità di stoccaggio. In questo contesto, il Laboratorio di Logistica 4.0 presso l’Università NTNU e l’azienda norvegese Wheel.me hanno introdotto una nuova soluzione basata sull’uso di ruote autonomatizzate per spostare gli scaffali di stoccaggio in qualsiasi direzione. Studi precedenti su questa nuova soluzione si sono concentrati sul design del sistema e sul movimento degli scaffali. Questa tesi si propone di valutare l’effetto della suddivisione dell’area di stoccaggio in due classi e dell’allocazione degli articoli più richiesti vicino al punto di ingresso/uscita del sistema sul tempo di ciclo. Per valutare l’efficacia di questa configurazione, è stata condotta un’analisi di sensibilità in cui sono stati variati diversi parametri, come la curva ABC, la forma della classe A, le dimensioni e il layout del sistema. I risultati mostrano significative riduzioni nel tempo di ciclo medio, con miglioramenti fino al 150% rispetto all’allocazione casuale, a seconda della configurazione specifica del sistema e delle caratteristiche degli articoli stoccati.
A puzzle-based movable rack system with class-based storage policy
FEDE, GIULIA
2022/2023
Abstract
The recent growth in e-commerce during the COVID-19 pandemic has intensified the demand for efficient warehouses with high storage density and throughput. Puzzle-based storage solutions have demonstrated their effectiveness in optimizing storage capacity with limited spaces. To further enhance these capabilities, the Logistics 4.0 Lab at NTNU and Wheel.me have introduced a new system that utilizes autonomous wheels, enabling storage racks to move in any direction. This innovative approach holds the potential to significantly improve throughput capacity. Existing studies have primarily focused on evaluating retrieval performance based on factors like escort locations, I/O points, and movement constraints. This paper aims to enhance retrieval time by implementing a two-class-based storage policy. By strategically placing high-turnover items near the I/O point, travel distances are minimized, resulting in faster retrieval. To evaluate the effectiveness of the class-based storage system, various input parameters are considered, including the ABC curve, system size, class A shape, and shape ratio. The objective is to examine how the optimized system with a class-based storage policy, impacts the performance of the new configuration. The findings reveal significant reductions in cycle time, with improvements of up to 150% compared to random storage, depending on the specific system configuration and characteristics of the stored items.File | Dimensione | Formato | |
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Executive_Summary_Fede_Giulia_10667255.pdf
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Master_Thesis_Fede_Giulia_10667255.pdf
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https://hdl.handle.net/10589/207207