Determining the Key Factors and Estimation of Fuel Consumption in Cold Chain Logistics: A Machine Learning Approach

This study investigates the key factors influencing fuel consumption in cold chain logistics (CCL) and presents a machine learning approach to estimate and optimize fuel usage. By analyzing data from various sources, the research identifies significant variables affecting fuel consumption, including vehicle age, maintenance frequency, temperature control settings, route characteristics, and load management. The findings highlight the importance of leveraging advanced technologies and machine learning models to enhance fuel efficiency, reduce costs, and improve environmental sustainability in CCL operations. Various linear regression models were tested to identify the best predictive solution, ensuring accurate and reliable estimates of fuel consumption under different conditions. This rigorous testing process helps identify the most effective strategies for minimizing fuel use. This approach paves the way for more sustainable and efficient logistics operations, ensuring adaptability and competitiveness in a rapidly evolving market.