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The vehicle routing problem is a central issue in transportation planning and optimization systems. The objective is to determine the most effective routes for a fleet of vehicles in order to service a set of geographically distributed customers while minimizing costs and adhering to capacity restrictions. Due to its inherent complexity, many heuristics have been proposed to solve this combinatorial problem in an effective way. In this paper, a novel hybrid algorithm that combines the Ant Colony Optimization (ACO) meta-heuristics with two local optimization heuristics, namely two-opt and lambda-interchange, is proposed. Experiments are conducted for seven benchmark instances of the vehicle routing problem in order to set up the ACO parameters, with which the reported results are generally outperforms other meta-heuristics. In addition, the proposed hybrid ACO is applied to solve a real life mail delivery network problem. The results from applying the proposed algorithm show that improvement has been achieved in terms of reducing the total distance by 11.7% and minimizing the total over time by 51.4%.