Optimization of multi-objective simulation of excavator-truck loading system for mining minerals

The shovel-truck loading system is one of the most important components of transportation in an open pit mine. To evaluate the performance of the excavator-truck system, the simulation modeling approach is combined with meta-heuristic methods and it has become a suitable approach to study and optimize the complex behavior of such a system. The purpose of this study is to identify the approximate optimal number of trucks and shovels in the equipment dispatch system in Sarchesmeh copper mine in Kerman province to increase monthly efficiency and reduce transportation costs. Two evolutionary multi-objective optimization algorithms, namely Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Fast Pareto Genetic Algorithm (FastPGA), have been programmed and integrated with the shovel-truck system simulation model developed in Arena software package to perform simulation, programming and integration. . The optimization process of the experimental results shows that there are near-optimal solutions that can reduce the average monthly transportation cost by 10% and increase the average monthly power by 11%.