Abstract: In this paper, we investigate the logistics optimization problem of plant-wide production processes in steel industry, which is to decide the amount of material flow between steelmaking, continuous-casting, hot rolling and cold rolling processes such that the constraints on unit's capacity, the storage capacity and the customer's demands are satisfied and the logistics cost, penalty of production capacity loss and stock cost are minimized. A mixed integer programming (MIP) model is formulated, then a column generation based optimization solution method is developed according to the model's characters. In the solution method, we first use the Dantzig-Wolfe decomposition method to split the MIP model into a master problem which has a simple structure but contains numerous columns and 4 types of subproblems which describe the vector space of the columns. Then, the column generation algorithm starts from a restricted version of master problem which contains only a subset of columns, and executes iterative operations between the restricted master problem and subproblems to obtain the optimal solution of the linear relaxation of the master problem. Finally, a branch-and-price algorithm, which is a branch and bound framework by embodying column generation as a bounding estimation mechanism, is designed to obtain the integral optimal solution for the original problem. Computational experiments are carried on a set of instances which are randomly generated based on the practical production data collected from a large steel company in China. The results demonstrate that the proposed algorithm can obtain the optimal or sub-optimal solution within a reasonable CPU time.