This thesis focuses on optimizing transportation planning by detecting and addressing paradoxical situations that can reduce transportation costs. Using Pyomo and the GLPK solver, the research demonstrates how supply and demand changes can lead to cost reductions contrary to traditional logistics expectations. A Python code was developed to implement the paradox detection, offering solutions for minimizing transportation costs in various scenarios. The work connects theoretical concepts with practical applications in logistics, providing insights for improving efficiency and sustainability. Ultimately, the findings underscore the importance of optimization in enhancing transportation systems and reducing costs in the logistics industry.