Recent worldwide disruptions, including supply chains, geopolitical tensions, and shifting trade policies, have underscored the strategic importance of effective and flexible warehouse logistics. This work outlines an approach that is transferable to multiple contexts for the design of warehouse configurations and storage policies using simulation, with the specific objective of maximizing the use of available areas, improving the efficiency of operations, and enabling responsiveness to seasonal fluctuations in demand during the year. The model takes into account the classification of items based on their rate of turn, strategic location of storage, and simulation modeling reinforced with key performance indicators (KPIs) to provide data-based decision support. An example is demonstrated through its application in an Italian warehouse, with the specific responsibility of distributing drinks in the geographically remote island of Sardinia, with the complications of seasonal fluctuations in demand patterns. Using the AnyLogic simulation, this study analyses multiple storage layouts and assesses a flexible, mobile rack system to enhance receiving efficiency, utilise floor space effectively, and reduce handling distances. Results emphasize the practical relevance and broad transferability of this new approach to multiple contexts of warehouse operation.
Simulation-Based Assessment of Warehouse Logistics: A Case Study in Beverage Distribution
Tsega Y. Melesse;Jacopo Sanna;Mattia Braggio;Mohamed Shameer Peer;pier francesco
2026-01-01
Abstract
Recent worldwide disruptions, including supply chains, geopolitical tensions, and shifting trade policies, have underscored the strategic importance of effective and flexible warehouse logistics. This work outlines an approach that is transferable to multiple contexts for the design of warehouse configurations and storage policies using simulation, with the specific objective of maximizing the use of available areas, improving the efficiency of operations, and enabling responsiveness to seasonal fluctuations in demand during the year. The model takes into account the classification of items based on their rate of turn, strategic location of storage, and simulation modeling reinforced with key performance indicators (KPIs) to provide data-based decision support. An example is demonstrated through its application in an Italian warehouse, with the specific responsibility of distributing drinks in the geographically remote island of Sardinia, with the complications of seasonal fluctuations in demand patterns. Using the AnyLogic simulation, this study analyses multiple storage layouts and assesses a flexible, mobile rack system to enhance receiving efficiency, utilise floor space effectively, and reduce handling distances. Results emphasize the practical relevance and broad transferability of this new approach to multiple contexts of warehouse operation.| File | Dimensione | Formato | |
|---|---|---|---|
|
1-s2.0-S1877050926005090-main_warehouse.pdf
accesso aperto
Tipologia:
versione editoriale (VoR)
Dimensione
1.08 MB
Formato
Adobe PDF
|
1.08 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


