## 2 Related Works
[PERSON] [39] developed a study that analyzed the functionality and performance of semi-automatic operation in loading and hauling equipment used in underground mining. [PERSON] [40] proposed a suitable method for controlling the loading and transporting of ore in a mine with a piles and \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Characteristics**} & \multicolumn{2}{c}{**Objective Function**} & \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**Num. Machines**} & & & & & \\ \cline{2-10}
**Reference** & \(Q_{m}\) & \(r_{i}\) & \(C_{max}\) & \(k\) & **Other** & **Exact** & **Approximate** & \(\mathbf{m\leq 5}\) & \(\mathbf{m\geq 6}\) & **Complexity** & **Approaches** \\ \hline [25] & & & & x & x & & & x & & MIP formulation \\ \hline [14] & & & x & & x & & x & x & x & MIP formulation with \\  & & & x & & x & & x & x & x & & independent jobs \\ \hline [26] & x & & x & & x & & x & & & MIP formulation with \\  & & & & & & & x & & & & MIP formulation \\ \hline [12] & x & & & & \(p\) & x & & x & & & MIP formulation \\ \hline [27] & & & & x & & & x & x & & & Monte Carlo simulation model \\ \hline [9] & & x & x & & & x & x & & & VNS and ACO algorithms \\ \hline [28] & x & & x & & x & x & & x & & MIP and TSP algorithm \\ \hline [29] & & & x & & x & & x & & & Constraint programming model \\ \hline [30] & & & & x & x & x & x & & & MIP formulation and \\  & & & & & & & & & & MOBSO algorithm \\ \hline [31] & x & & & x & x & x & x & & & & MILP formulation with time-of-use \\  & & & & & & & & & & and SPT, MDPC, MDEC \\ \hline [32] & x & x & x & & x & x & & x & & MIP formulation and GA–GWO \\  & & & & & & & & & & hybrid algorithm \\ \hline Our paper & x & x & x & x & x & & x & x & x & Iterative MIP formulation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)chambers system. [PERSON] [41] developed a scheduling model for surface mining, focusing on cost reduction. [PERSON] [42] modeled ore loading and hauling systems in surface mining, developing a model that considered the excavation face, routes, equipment, and ore destinations. [PERSON] and [PERSON] [43] analyzed utilization and availability indicators for ore hauling cost optimization. [PERSON] et al. [44], in their research for underground mining, developed a bee swarm algorithm to maximize the total revenue during a planning period; in addition, they created a genetic algorithm for the planning for hauling equipment dispatching to minimize the waiting time of this equipment. [PERSON] [45] developed a mathematical model for calculating the arrival and service rate of different amounts of hauling equipment involved in material transportation. [PERSON] [46] improved the use of loading and hauling equipment to reduce the cost of these operations. [PERSON] et al. [47] developed an intelligent non-supervisory system to predict the performance of the hauling equipment system in surface mining using a combination of different optimization models. [PERSON] et al. [48] focused on a mathematical model for rescheduling operations considering the requirements of production spaces, operating environment, and production equipment wear, while seeking to obtain the maximum planning completion rate and the lowest ore grade fluctuation. [PERSON] et al. [49] presented a multi-objective optimization in the surface mine system, seeking the maximum amount of extraction and minimizing the transportation time, considering the storage capacity, transportation equipment, and budget. [PERSON] et al. [50] performed an IP with operational restrictions to schedule the transportation system in surface mining, considering the best net present value as an objective. [PERSON] et al. [51] addressed the energy-efficient unrelated parallel machine scheduling problem, incorporating job deterioration and the learning effects of operators, providing valuable insights into multi-objective optimization for complex production environments.