Optimization of logistics costs through a transport model in a company in the Fishing Sector

Abstract

This study explores logistics cost optimization in a fishing company in Chimbote, Peru, which faced an 18% rise in distribution expenses. The increase was due to two key issues: suboptimal transport routes and underutilized fleet capacity, with 35% of the fleet remaining idle. To address this, a linear programming optimization model was developed. Its main innovation lies in incorporating industry-specific variables—such as cold chain constraints and product shelf life—into the design of transport routes to distribution centers in Lima, Callao, Piura, Paita, and Chiclayo. After implementation, the model led to a 10–15% reduction in logistics costs and a clear improvement in operational efficiency. A sensitivity analysis confirmed the model’s robustness under scenarios involving fuel price fluctuations, raw material supply variations, and demand spikes. In conclusion, this research not only offered a practical and cost-effective solution for the company studied but also introduced a replicable methodological framework. This framework can be adapted by other perishable food companies in emerging economies to improve logistics performance, ensuring sustainability and competitive advantage. The study highlights the strategic value of integrating advanced optimization tools into supply chain decision-making within the perishables sector