Intelligent logistics route optimization: Impact of Dijkstra's algorithm on operational efficiency (#1593)

Authors

Cuya Camara, Jose Luis

Abstract

Efficient route planning is key in logistics to reduce costs and improve operational capacity. This study evaluates the impact of Dijkstra’s Algorithm on logistics route optimization through a pre-post experimental design with a quantitative approach, applied in an Integrated Optimal Route Planning System developed with the agile SCRUM methodology. 58 logistics processes were analyzed, measuring three key indicators: order delivery time, route planning and report generation, and Student’s t-test was used to assess statistical significance. The findings show significant improvements: reduction of delivery time by 22.94% (from 88.73 to 68.37 minutes), optimization of planning time by 97.2% (from 7684.8 to 217.07 seconds) and decrease of reporting time by 99.89% (from 6504.57 to 6.83 seconds). Statistical analysis confirmed that all improvements were statistically significant (p-value<0.001), validating the effectiveness of the algorithm in logistics optimization. It is concluded that the implementation of Dijkstra's Algorithm improves operational efficiency, optimizes real-time decision making, and has the potential to reduce logistics costs and minimize environmental impact. As future lines of research, its integration with artificial intelligence and machine learning is suggested to improve adaptability in dynamic environments.