A design method for agricultural product logistics cold chain distribution routes based on a genetic algorithm

In this study, a new methodology is developed to optimize cold chain distribution routes of agricultural products with a view to reducing the costs of distribution. It develops a mathematical model that considers multiple costs, including fixed costs, transportation costs, cooling costs, cargo loss, and time penalties, all of which have been addressed in separate studies in previous research works. By incorporating a genetic algorithm (GA) into the mathematical model of this study, it is possible to optimize the distribution routes of agricultural products for Company Y across 16 cities, while considering the Vehicle Routing Problem with Time Window constraints. This study is unique in its methodology as it considers a diverse range of costs in an integrated manner that makes it realistic and practical at the same time. This study is also realistic in its objectives as it aims to develop a model that minimizes the total costs of distribution by 25.87% while reducing the costs of time penalties by 41.98%. Moreover, analysis of vehicle speed also indicates that an optimized truck speed of 60 km/h is helpful in reducing costs of distribution while improving truck performance in terms of 52.58%–94.72% increased loadability as well as 352.6 km lower mileage. • Proposes a genetic algorithm-based model to optimize cold chain distribution of farm products. • Cuts total expenses by 25.87%, with time penalty costs reduced by 41.98%. • Improves truck loading from 52.58% to 94.72% and reduces mileage by 352.6 km. • Enhances logistics efficiency while ensuring the freshness of agricultural products.