Hybrid Algorithm for Solving the Heterogeneous Fixed Fleet Vehicle Routing Problem

In this study, the heterogeneous fixed fleet vehicle routing problem is considered. A new hybrid algorithm is proposed which is based on Gasimov's sharp augmented Lagrangian method described in 2002. Modified subgradient algorithm uses the sharp augmented Lagrangian by considering the zero duality gap condition. A new hybrid algorithm which combines modified subgradient algorithm based on feasible values (F-MSG) and genetic algorithm, is analyzed to solve the heterogeneous fixed fleet vehicle routing problem. Since the genetic algorithm is an effective population based metaheuristic algorithm in the literature for combinatorial optimization problems, new hybrid method can deal with the NP-hard structure of the vehicle routing problem. The performance of the hybrid algorithm is analyzed and compared by using test instances from the literature. The computational results indicate the advantage of combining the modified subgradient algorithm and the genetic algorithm more clearly.