Multi-Factor Coupled Improved Dual-Coded Genetic Algorithm: Low-Carbon Routing Optimization in Fresh Produce Logistics with Dynamic Speed

To address traditional genetic algorithms’ limitations in multi-factor coupling scenarios, such as insufficient adaptability to complex constraints and reliance on single-dimensional coding mechanisms, this study proposes a Multi-Factor Coupled Improved Dual-Coded Genetic Algorithm (MF-IDGA) specifically for perishable logistics, and constructs a low-carbon routing optimization framework integrated with dynamic speed constraints to tackle multi-objective challenges under dynamic traffic conditions. Key improvements include dual coding integrating path and speed parameters, multi-factor coupling mechanisms, and optimized evolutionary strategies. Experimental results show that MF-IDGA reduces total cost by 14.9% and carbon emissions by 13.1% compared to traditional fixed-speed models and single-coding genetic algorithms, converging stably within 600 iterations, thus providing efficient algorithmic support for low-carbon routing optimization in perishable logistics under dynamic traffic environments.