# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 1 Introduction  
Many countries have increasingly prioritized the advancement of intelligent coal mines in recent years [1], resulting in improvements in intelligence, carbon footprint, and efficiency levels in the coal mining industry. Coal loading has transcended traditional methods. Numerous researchers have delved into intelligent coal loading systems using sensing, navigation, and communication technologies [2]. Nevertheless, as an important part of coal intelligent loading systems, studies on pre-loading vehicle scheduling are necessary. Presently, the majority of coal enterprises rely heavily on manual determination of loading time and location (loading bunker serial number), which is a subjective process devoid of scientific guidance. Furthermore, the varying needs, loading capacities, and priorities of multiple customers, combined with manual scheduling practices, result in disorderly vehicle queues and excessive waiting times. As a consequence, numerous issues arise, including elevated carbon emissions, diminished loading efficiency, and decreased customer satisfaction [3]. Consequently, investigating optimization methods for multi-customer vehicle scheduling (MCVS) in coal intelligent loading systems has emerged as a critical issue. Many scholars have conducted vehicle scheduling optimization research in diverse fields utilizing various methods. [PERSON] et al. [4] devised a multi-objective intelligent scheduling model for unmanned vehicles in surface mines, aiming to ascertain the optimal number of vehicles for dispatch and the traffic flow scheduling scheme. [PERSON] et al. [5] explored the optimization of multi-automated vehicle scheduling in surface mines, considering both temporal and spatial factors. [PERSON] et al. [6] took into account vehicle travel and speed, and proposed a real-time scheduling system for automated trucks in a stochasticdynamic mining environment. [PERSON] et al. [7] suggested an intelligent distribution strategy for all mineral materials within an underground coal mine auxiliary transportation system. [PERSON] et al. [8] introduced a global scheduling strategy utilizing the Baidu industrial solver for auxiliary vehicle scheduling of trackless rubber-wheeled vehicles in underground coal mines. Overall, vehicle scheduling has emerged as a significant research focus across multiple disciplines. In the coal industry, research primarily concentrates on vehicle scheduling within open-pit coal mines and underground auxiliary transportation; however, studies focusing on customer vehicle scheduling before coal intelligent loading remain relatively limited. Customer priorities are essential in MCVS, as they directly impact both the vehicle scheduling scheme and customer satisfaction. Numerous scholars have conducted thorough research on customer priorities in scheduling issues. [PERSON] et al. [9] developed a comprehensive evaluation model for an open-pit mine vehicle scheduling system utilizing grey relation analysis and the ideal similarity ranking preference technique and determined the final weighting combination for each factor based on game theory. [PERSON] et al. [10] applied a combination of grey relation analysis and the entropy weight method to address the multi-objective solving challenge in multi-automatic guided vehicle dispatching operations under a composite operation mode. [PERSON] et al. [11] tackled the optimization problem in cold chain delivery routes by incorporating customer priority into the analysis through the utilization of a clustering algorithm. [PERSON] et al. [12] addressed prolonged queuing times for hospital patients by optimizing with a focus on priority queue principles, leading to a considerable enhancement in patient satisfaction. [PERSON] [13] introduced a prioritized list approach for decision-making in ambulance dispatch to various customer types, considering ambulance availability as a key factor.