Multi-objective model of fuzzy robust to reduce pollution caused by CO2 gas in open pit mines: comparing the impact of deterministic and uncertain data

Today, in open pit mines, the issue of stone transportation from the mine to the place of discharge is of particular importance. In open-pit mines, there is uncertainty in the transportation speed and the time when trucks arrive at the loading place, in terms of the changes in weather conditions, and the changes in the physical and mental conditions of human resources. Such uncertainty will lead to the increased pollution caused by CO2 emissions in the movement and speed of trucks. In order to save production and environmental costs, this study proposes a multi-objective mathematical model to organize the distribution of trucks to various loading sites in the mine. The model was solved using the epsilon constraint approach, and the uncertainty was resolved using fuzzy robust optimization. As the real study, the Chadormalu mine was selected and the transportation planning for one work shift was studied. The case study results show that while bigger trucks lead to higher pollution costs, they can also lower overall costs, encompassing transportation and pollution, in uncertain environments. The sensitivity analysis on the value of the main variables and parameters shows that to deal with the uncertainty and time and speed change of the trucks, using more trucks that have higher capacity is needed.