Abstract
The vehicle routing problem (VRP) has received a lot of attention and has been applied extensively in the field of logistics. In practice, the heterogeneous fleet is usually used at the distribution center. The need for different types of vehicles is determined by the characteristics of the customers. Hence, the heterogeneous fleet vehicle routing problem (HVRP) which minimizes the sum of fixed vehicle costs and variable routing costs is an important variant of VRP. The HVRP is a NP-hard combinatorial optimization problem that is very difficult to solve optimally within reasonable time. The ant colony optimization (ACO) algorithm has been applied to the VRP successfully, but much less for VRP variants. Therefore, this research aims to develop a two-stage ant colony optimization (TACO) algorithm to solve the HVRP. The TACO was tested on two sets of benchmark instances in the literature. The results show that the performance of the TACO is competitive with the state-of-the-art algorithms.
