Efficient goods distribution planning is vital to ensure high business revenues for logistics operators and minimize negative impacts on the environment. In this thesis, we address three main problems related to goods distribution planning in urban areas namely customer allocation, order scheduling, and vehicle routing. A three step approach is proposed.  In the first step, we use Nearest Neighbour and Tabu Search for balanced allocation of customers to logistics depots. In the second step, Genetic Algorithm approach is used to perform order scheduling at each depot for the allocated customers. In the third and the last step, we perform vehicle allocations and generate fastest paths for goods delivery to customers using modified Dijkstra’s algorithm. All these decisions are made considering realistic conditions associated with goods distribution in urban areas such as presence of congestion, municipal regulations, for example vehicle sizing, timing and access regulations etc. The objective is to minimize total distribution costs of logistics operators under these constraints.
A prototype decision support system is developed integrating the proposed approaches for goods distribution planning in urban areas.  The strength of the proposed decision support system is its ability to generate fast and efficient solutions for balanced customer allocation, dynamic order scheduling, vehicle allocation considering environmental constraints and fastest path generation under dynamic traffic conditions. The proposed model results are verified and validated against other standard approaches available in literature.