Logistica applicata: risolvere problemi di flusso di rete utilizzando python

This thesis delves into the world of supply chain management, focusing on transportation logistics, and aims to design and apply optimization models for planning transportation routes that meet logistics objectives and challenges. The first part of the thesis provides an overview of the fundamental concepts in logistics and operations research, a field that deals with making optimal decisions. Moreover, it introduces linear programming to formulate optimization problems such as minimum cost flow and multi-commodity flow problems. The second part reports the practical implementation of the problems addressed during my internship experience. It illustrates the development of a tool to analyze a transportation network and visualize different route scenarios by solving optimization problems, in order to gain a detailed and insightful understanding. The tool is based on Python and linear optimization libraries to analyze the historical data with the purpose of being integrated into a working outbound system. Its implementation will contribute to the field of logistics and transportation management, improving the planning process, reducing delays, and enhancing overall delivery times and costs. This thesis demonstrates the potential of linear optimization models for transportation logistics and provides a practical and flexible tool that can be adapted to different scenarios and objectives. The results show that the tool can improve the efficiency and effectiveness of the transportation network, and suggest further improvements and extensions for future work.