This paper studies the production and distribution planning problem faced by the iron ore mining companies, which aims to minimize the total costs for the whole production and distribution system of the iron ore concentrate. The ores are first mined from multiple ore locations, and then sent to the corresponding dressing plant to produce ore concentrate, which will be sent to distribution centers and finally fulfill the customers' demands. This paper also tackles the difficulty of variable cut-off grade when making mining production planning decisions. A mixed-integer programming model is developed and then solved by a Lagrange relaxation (LR) procedure. Computational results indicate that the proposed solution method is more efficient than the standard solution software CPLEX.

With the rapid development of information technology, large-scale data is collected and stored, which provides a huge amount of information for decision-making. This paper focuses on the planning of mine supply chain under the big data. The mine supply chain usually contains three stages, which is mining, processing, and ore product transportation. This paper tackles the difficulty of variable cut-off grade by establishing a robust optimization model. To solve the robust optimization model, the nonlinear constraints in the model were linearized first. Then, the specific parameter values were determined through the employment of the hypothesis test in statistics, and the robust optimization model was solved finally. The analysis results show that the robust optimization model can be stabilized when the parameters are subject to disturbance. Finally, sensitivity analysis experiments are carried out for several parameters in the model to find out the influence of each parameter on the model. This paper combines mine supply chain planning with big data, which not only improves the production and transportation efficiency of ore products, but also reduces related costs.