Strategic resource allocation through route optimization: Enhancing operational efficiency and competitive advantage in distribution operations

Abstract

This research addresses the optimization of distribution routes to reduce transportation costs in an ice cream company in Chepén, Peru, aligning with Sustainable Development Goal (SDG) 12 (Responsible Production and Consumption). The study employed an applied, quantitative, pre-experimental design with an explanatory scope. The population consisted of one year’s distribution records, with a sample of eight weeks. Data collection used documentary analysis through standardized forms. An Ishikawa diagram identified 13 root causes of high transportation costs, with the Pareto principle prioritizing nine causes representing 79.78 percent of the total impact. The main cause identified was non-optimized routes. Using the traveling salesman problem (TSP) model with Miller-Tucker-Zemlin (MTZ) restrictions implemented in Excel Solver, routes were optimized by merging and reconfiguring paths. Results showed an 18.14 percent reduction in average weekly transportation costs (from S/7,365.47 to S/6,029.51), with annual savings estimated at S/69,500. The Student’s t-test confirmed the statistical significance of this improvement (p < 0.05). This research demonstrates that route optimization through linear programming significantly contributes to reducing operational costs while promoting sustainable resource use by minimizing fuel consumption, tire wear, and carbon emissions. These findings are consistent with prior evidence that route optimization is a critical lever for logistics performance (Álvarez et al., 2024) and with case-based studies in similar contexts (Rodríguez et al., 2023).