Università degli studi di Modena e Reggio Emilia

Intra-logistics optimization plays a crucial role in ensuring efficiency and reducing non-value added activities, especially in scenarios with a central shipping point and multiple peripheral warehouses. The goal of this study is to create an automated and optimized Decision Support System (DSS) using an integer linear programming (ILP) model. The DSS optimizes the order management process by determining optimal load configurations from peripheral warehouses onto transport vehicles. The resulting transportation plan, generated through this approach, aims to meet customer demands while minimizing overall costs. Computational tests, conducted on a real-world case study, validated the efficiency of the proposed system.

This study aims at identifying the best strategy to temporarily store products within a buffer area in an Italian ceramic tile company. The storage policy is analyzed to maximize the storage capacity, facilitate operators' activities, and, consequently, improve the warehouse logistics performance. A discrete event simulation was conducted using Salabim, a Python based open-source software, in order to determine the best policy. We compare the performance of the current storage policy, based on technical production properties of products, and a newly proposed one, based on products' downstream destination. The results suggested that the proposed strategy significantly improves the performance of the buffer area management. The approach can be applied to different applications, contributing to the literature on simulation-based decision-making in material management. Furthermore, the study provides a functional case study showing the potential and achievable results of Salabim for modeling complex systems.