Logistics Efficiency in Product Distribution with Genetic Algorithms for Optimal Routes

Abstract

This research aims to optimize product distribution routes in logistics using computer simulation approaches and genetic algorithms. This research produces more efficient distribution routes by utilizing mathematical models that reflect actual distribution processes, including variables such as warehouse locations, distribution points, product types, customer demand, and vehicle availability. Genetic algorithms are used to design optimal solutions with implementation stages, which include solution representation, population initialization, fitness evaluation, selection, crossover, mutation, and stopping criteria. The visualization results show that the genetic algorithm can produce more structured and efficient distribution routes, reducing total travel distance, distribution costs, and delivery time. Statistical analysis supports significant improvements in distribution performance after implementing the genetic algorithm, with substantial reductions in total mileage, distribution costs, and delivery times and substantial improvements in customer satisfaction. Financial analysis shows significant cost savings and positive ROI from investing in genetic algorithms, while sensitivity analysis reveals the impact of critical factors on distribution costs. This research confirms the financial and operational benefits of applying genetic algorithms in product distribution optimization, with significant efficiency, cost savings, and customer satisfaction results.