In this thesis, we consider a multicommodity distribution problem. We assume that there is a central depot which houses a number of different types of items. There is a finite number of geographically dispersed demand points which place orders for these items on a daily basis. The demand of these demand points should be satisfied from this central depot. We assume that a finite number of identical trucks with predetermined destinations are used for the distribution of the items from the central depot to each demand point. The demand of each demand point can be split among several trucks and a single truck is allowed to visit several demand points. Our objective is to satisfy the demand of each demand point with the minimum total distribution cost while respecting the capacity of each truck. The cost structure is dictated by the final destinations of trucks used in the distribution of the items and the set of demand points visited by each truck. We propose two different solution approaches. The first approach, called the Direct Approach, is aimed at solving the problem directly using a mixed integer linear programming formulation. Since the Direct Approach becomes computationally infeasible for real-life problems, we propose a so-called Hierarchical Approach that is aimed at solving the problem in two stages using an aggregation followed by a disaggregation scheme. We study the properties of the solutions computed with the Hierarchical Approach. We perform extensive computational studies on a data set adapted from a major automotive manufacturing company in Turkey in an attempt to compare the performances of the two approaches. Our results reveal that the Hierarchical Approach significantly outperforms the Direct Approach on the vast majority of the instances.