Research of Vehicle Scheduling and Path Planning for Concrete Batching Plant Based on GA-DeepACO-NLS Model

Concrete mixing stations play a vital role in urban infrastructure construction. In this process, efficient vehicle scheduling and path planning systems have a significant impact on improving concrete supply efficiency, reducing logistics costs and alleviating urban traffic congestion. To address this problem, this study proposes an innovative vehicle path planning method, namely a comprehensive optimization strategy based on the GA-DeepACO-NLS model. This model cleverly combines the technologies of deep reinforcement learning, ant colony algorithm and genetic algorithm form a powerful multi-objective optimization framework. Through this fusion, the model is able to effectively deal with complex vehicle path planning problems. In practical applications, through a series of carefully designed experiments, we verified the advantages of this model in reducing the total distance traveled by the vehicle and improving the efficiency of task completion. Experimental results show that compared with existing path planning methods, the GA-DeepACO-NLS model has achieved significant improvements in multiple evaluation indicators. This not only provides a new solution to the vehicle path planning problem of concrete mixing stations, but also provides valuable reference for research and practice in related fields. The GA-DeepACO-NLS model proposed in this study provides an efficient and intelligent solution to the vehicle path planning problem of concrete mixing stations, which helps to promote the sustainable development of urban construction projects and contributes to urban planning and traffic management, it also provides strong technical support for urban planning and traffic management.