In this paper, we investigate the charge hatching planning problem (CBP) arising from practical steelmaking production. The CBP transforms the primary order requirements into various production batches (charges) subject to the steelmaking processing constraints and composite batch conditions according to the similarity in steelgrade, dimension, physical property, and due-date of orders. On the basis of a practical steelmaking process, a novel mixed-integer programming model for the CBP is presented by considering above constraints and features here, and two kinds of Lagrangian relaxation (LR) methods are proposed to solve the CBP by using different relaxation methods. In the first LR method, the relaxed problem is separated into subproblems by relaxing assignment constraints which are solved optimally by dynamic programming. In the second method, variable splitting is presented by introducing identical copies of some subsets of the original variables. To guarantee the equivalence to the primal problem, a number of equality coupling constraints are added into the model which are relaxed during the course of the second Lagangian relaxation. The multipliers in all above LR methods are then iteratively updated along subgradient directions. Computational experiments have been carried out and the results show that both LR methods can produce satisfactory average duality gaps and the second LR method is little better than the first method.