Intelligent Logistics Vehicle Scheduling Based on MPHIGA

The current intelligent logistics vehicle scheduling faces challenges, including the difficulty of obtaining real-time location data and the need for manual intervention in emergencies. To address these issues, a modified multi-population hybrid genetic algorithm is proposed, along with an intelligent scheduling model constructed through the reconstruction of domain generation strategies. Experimental results show that the model stabilizes the total cost at 7864 yuan within 49 iterations, whereas the dual-population hybrid genetic algorithm requires 51 iterations, making convergence more time-consuming. Moreover, when the scheduling frequency is two, the research model successfully allocates three company vehicles, whereas the comparison algorithm can only allocate two. Overall, the research model offers significant advantages in reducing operating costs and enhancing dynamic response capabilities, providing effective technical support for the digital transformation of logistics companies.