Internet-of-Things-Based Low-Carbon Distribution Route Optimization for Logistics

This study addresses the optimization of cold chain logistics distribution routes by proposing a low-carbon distribution route optimization model based on the Internet of Things (IoT) technology. Using IoT technology to monitor key data, such as temperature, humidity, and vehicle location along cold chain logistics distribution routes, the study dynamically optimizes the distribution process. Incorporating principles of low-carbon distribution, the model considers factors, such as fuel consumption, vehicle emissions, and transport efficiency to minimize carbon emissions and ensure environmentally friendly logistics operations. A genetic algorithm is employed as a path optimization tool, iteratively evolving toward optimal or near-optimal distribution routes, effectively reducing carbon emissions. The introduction of IoT technology extends route optimization beyond traditional cost and time considerations to include environmental impact and sustainability, reflecting the logistics industry’s commitment to green and low-carbon development. Actual distribution data from a cold chain logistics company in a specific city are collected and divided into training and test sets for model training and validation. The model is validated and analyzed through simulations and experiments under different scenarios. Experimental results demonstrate that the optimized distribution routes reduce total travel distance by an average of 10.2%, total energy consumption by 12.5%, and total carbon emissions by 12.8%. Additionally, the temperature fluctuation range is reduced by an average of 13.3%. The genetic algorithm employed in the study converges within 350 generations across all scenarios, with an average convergence time of 45 s. Hence, this study provides an effective solution for optimizing cold chain logistics distribution routes, offering significant practical value.