Improving the cost of vehicle routing problem by using ant colony optimization with clustering techniques

In multi-depot vehicle routing problems meeting the customer demand in the shortest time with the shortest path and appropriate number of vehicles is the major point for the solution of these problems. Many practical approaches and algorithms have been established for the solution of a kind of hard NP vehicle routing problems. These algorithms can be divided into two groups in general, to be precise and approximate. The bad side of the exact algorithms is that they show poor performance. But unlike the exact algorithms, many approximate algorithms reveal high-quality solutions in short time periods for combinational problems. One of these high-quality solution techniques is known as ant colony optimization. In this thesis ant colony optimization with k-means clustering technique is applied for the vehicle routing problem. The changes in the total realization of demands in time, total distance and total number of vehicles are observed for the applications.