A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System

Intelligent loading systems are extensively employed in coal enterprises. Nevertheless, pre-loading customer vehicle scheduling predominantly depends on manual expertise. This frequently results in extended vehicle waiting periods, elevated carbon emissions, and reduced customer satisfaction, particularly in multi-customer scenarios. Therefore, this study introduces a multi-customer vehicle scheduling optimization approach for an intelligent coal loading system. Customer priorities are first identified to enhance satisfaction. Considering various customers and enterprise factors, the multi-customer vehicle scheduling model is established to minimize the total cost. The optimal vehicle scheduling scheme is obtained by using the enhanced sparrow search algorithm. The validity of the proposed approach is demonstrated through a case study of a coal mining enterprise. The results show that the total cost of the optimized plan was 79% lower than the traditional plan, which means a significant reduction in vehicle waiting time, and an improvement in customer satisfaction.