## 1 Introduction  
The execution of daily production plans in underground metal mines is facilitated by a coordinated approach that includes mining, equipment installation, transportation, and strategic deployment of crushing machinery. The rational allocation of equipment and vehicles within the mine has a significant impact on overall production capacity and efficiency, which in turn has a significant impact on the economic profitability of mining operations. According to the research, taking the statistics of iron ore mines as an example, the production time of haul trucks accounts for only 70% of the total time [1]. This is due to the complex driving route, the complex types of running vehicles, and the narrow driving channel, which makes it difficult to optimize the transportation system. In addition, due to the blindness of traditional scheduling, the driving routes of trackless equipment are often conflicting and blocked, resulting in the stagnation of the transportation system. With the popularization of 5G mobile signal transmission technology in underground mines, the trackless transportation dispatching system of underground mines will usher in a new reform. Through real-time signal transmission, the dispatching center can determine the position and operation situation of the trackless equipment in real-time, so intelligent scheduling can be realized. Worldwide, some scholars have studied the intelligent scheduling algorithm of trackless equipment in underground mine transportation systems. A heuristic approach based on linear programming is used to minimize the waiting time at loading points for multiple trucks with different capacities [2]. This method, used to determine mass flow along the path, has been run in real-time applications in more than 10 mines and improves productivity by 10 to 20%. [PERSON] and [PERSON] proposed an enumeration algorithm based on dynamic programming for optimizing fleet management in underground mines [3]. The method arranges vehicles to move in a transport network consisting of single-lane bidirectional roads, aiming to find the route and schedule of each vehicle to minimize the movement time without conflict. [PERSON] et al. use transport network modeling and optimize the network model, providing information about transport distance and driving time. The transport distance is calculated from the length of the route center line based on the tunnel layout [4]. The model has a significant effect on reducing the operation cost of mines. [PERSON] et al. investigated how fast local search and parallel computation with the genetic algorithm can be used to solve the trackless transport vehicle path problem to minimize the number of vehicles and total transport distance or time [5]. This method not only improves the optimization capability in the global scope, but also ensures operational efficiency. Some people have also proposed an enumeration algorithm based on dynamic planning [6; 7]. The algorithm applies a multi-objective heuristic based on a two-stage Pareto local search and VNS (2 PPLS-VNS), multi-objective variable neighborhood search (MOVNS), and non-(NSGA-II) to job planning problems with dynamic truck assignments [8; 9]. The experiment demonstrates good convergence [10]. The aforementioned studies offer insights into potential avenues for intelligent scheduling algorithms in underground trackless transport equipment in mines. However, the majority of these studies focus on a single objective, with the primary aim being the reduction in route conflicts. The issue of bidirectional transport on the ramps of underground metal mines is distinct from that of scheduling mine trucks in open-pit mines. Inevitably, there will be instances of route conflicts involving trackless equipment. Furthermore, in the context of underground mine transport scheduling, focusing exclusively on route conflict issues while neglecting the necessity to adhere to passage priority in vehicle production will inevitably result in logistical inconsistencies.