Abstract: The inventory-transportation integrated optimization (ITIO) problem in a distribution network with multiple warehouses and multiple retailers is addressed. For solving this problem, different algorithms are explored. First, a Lagrange multiplier method is used to solve the integrated problem of inventory control and transportation scheduling. Then, to overcome the computationally inefficiency for large-scale problem by the Lagrange multiplier method, a scenario-based dynamic slope scaling procedure (DSSP) heuristic is proposed to establish an ITIO model. Lastly, to improve the solution accuracy of the heuristic, the Lagrangian relaxation-based DSSP heuristic is applied to solve the ITIO problem. Comparison is done for problems in a many-to-many distribution network. Results show that the Lagrangian relaxation-based DSSP heuristic outperforms the others in both solution accuracy and computational efficiency.