## 1 Introduction  
Due to the large area and complex terrain of surface coal mines, different sections of coal mining points and coal preparation plants loading coal require a corresponding number of coal mining vehicles and vehicles, so the scheduling and management of these coal mine vehicles have become a severe problem. The existing transport system presents slow-running vehicles, an extended waiting time, chaos in management, and other shortcomings in a modern mine; the modern production needs of coordinated scheduling, safe and efficient transportation, and intelligent resource deployment characteristics have become a hot spot for many coal companies and many scholars to pay attention to and research [1, 2]. Research statistics indicate that transportation costs of trucks in surface coal mines represent 45% to 60% of the total operating expenses. Consequently, effectively reducing these transportation costs and enhancing production efficiency are crucial for improving the overall productivity of mines. The study of transportation scheduling is a crucial method to enhance transportation efficiency, and optimising transportation scheduling can save \(3-20\%\) of the cost [3]. Given this, improving the efficiency of coal mine vehicle operation and effectively reducing the transportation cost of vehicles in the transportation process has become an essential issue for the surface coal mine's imminent solution. The vehicle scheduling problem involves managing a fleet of multiple vehicles, a distribution centre (or warehouse), and a set number of customers. It requires planning optimal driving routes while adhering to various constraints, such as goods quantity, time limitations, distance restrictions, and vehicle load limits. The objective is to achieve optimal outcomes such as minimising costs, reducing travel distances, decreasing delivery times, and using the fewest number of vehicles possible [4]. Surface coal mine truck scheduling is a complex system engineering problem; the truck scheduling model scheduling objectives of the unitary are an important reason affecting the accuracy of the model; the actual coal transportation is a multi-factor interaction of engineering; multiple optimisation scheduling objectives interact with each other to influence the constraints of the distribution of trucks in a motorised distribution [5] model. In 2017, [PERSON] et al. [6] examined the congestion levels of surface mining transport routes and their road capacities. This study aims to address the existing gaps in knowledge regarding road resistance, establish a uniform distribution model of the open pit mine traffic road network considering road access saturation, realise the re-optimisation of the scheduling scheme when the road is congested, and improve the production applicability of the scheduling model. [PERSON] et al. [7] studied the scheduling model to minimise shovelling empty time, truck waiting time, and deviation from the production optimisation path. They proposed a real-time truck scheduling multi-objective transportation model that makes the optimal decision to handle the material through path production optimisation and real-time truck scheduling, which meets 85% of the production requirements of truck operation in the actual production state. In contrast, the number of mining trucks is reduced by 30% compared to the number required. [PERSON] et al. [8] established a corresponding mathematical model for the low-carbon transportation scheduling problem of open-pit mines to minimise the sum of carbon emission costs and transportation costs. The coal mine transportation scheduling problem is primarily a multi-objective function optimisation issue focusing on maximising value. In recent years, inspired by natural organisms, many scholars have used intelligent swarm search algorithms to solve the coal mine truck transportation scheduling problem. [PERSON] [9] and [PERSON] [10] designed improved ant colony algorithms to solve the underground vehicle path problem. [PERSON] et al.