## 1 Introduction
[13] proposed a multi-objective, multi-modal combinatorial optimization problem aimed at minimizing route distances and the number of trucks. [PERSON] et al. [14] introduced a multi-objective mathematical model to minimize manufacturing span, transportation costs, and the number of truck trips in the supply chain. [PERSON] et al. [15] constructed a service network-based multi-objective integer programming model for port container truck scheduling optimization, with the objectives of maximizing the container volume in consolidated distribution, minimizing total transportation costs, and minimizing truck scheduling volume. [PERSON] et al. [16] proposed a new bi-objective mixed-integer linear programming model aiming to minimize total operational costs and the sum of maximum early and late arrivals. The establishment of a scheduling model based on key scheduling objectives is critical for optimizing the scheduling of battery-swapping trucks [3; 4]. Some researchers have considered the actual production conditions of mines and analyzed the impact of other factors on mining scheduling schemes, thereby establishing more suitable scheduling models for open-pit mining trucks. [PERSON] et al. [17] developed a real-time scheduling model consisting of two parts, taking into account both loaded and unloaded truck scheduling. [PERSON] et al. [18] proposed the following two new scheduling procedures: the 'Earliest Truck Loading Completion' procedure and the reallocation of shovel-truck scheduling under imbalanced conditions. [PERSON] et al. [19] constructed a comprehensive evaluation factor model for open-pit mine truck scheduling systems from the dimensions of optimal routing, traffic flow planning, and real-time scheduling, and established a comprehensive evaluation model for open-pit mine truck scheduling systems based on gray relational analysis technology. [PERSON] et al. [20] proposed an optimization model that minimizes the fuel consumption of dump trucks and shovels in truck-deficient open-pit mines while meeting the loading and unloading requirements of the dump site. [PERSON] et al. (2018) constructed a real-time scheduling model compatible with different truck load requirements using flow network and integer programming techniques. [PERSON]. [PERSON] et al. (2018) proposed a balanced distribution model for open-pit mine truck scheduling based on traffic flow planning. [PERSON] et al. (2018) introduced a new truck scheduling model using fuzzy linear programming to address the underestimation of interactions among truck fleets, shovel fleets, and processing plants in published models, and to account for strategic-level planning goals. Additionally, no existing model addresses the uncertainties related to input parameters. [PERSON] et al. (2018) developed a multi-objective transportation model for real-time truck scheduling, addressing two major shortcomings of previous models. The proposed model schedules trucks to destinations while simultaneously minimizing shovel idle time, truck waiting time, and deviations from path production requirements established in the production optimization phase. [PERSON] et al. (2018) considered loading and unloading logic relationships, work time constraints, loading and unloading volume constraints, traffic flow constraints, and loading and unloading capacity constraints to maximize total loading and unloading volume. A 0-1 decision variable-based truck scheduling model was developed, with truck operations between loading and unloading sites as the decision variables. [PERSON] et al. (2018) proposed a solution based on a multi-agent system (MAS), where equipment items are represented by intelligent agents that interact to achieve production goals at the lowest cost. [PERSON] et al. (2018) introduced a multi-stage scheduling approach to solve real-time truck scheduling problems in open-pit mining operations through allocation planning and dynamic assignment. [PERSON] et al. (2018) considered the issue of ore blending and, along with [PERSON] et al.