Ant Colony Optimization for Heterogeneous GVRP with Customers Service Restrictions

Abstract

Carbon dioxide emission has become a serious issue all over the world. Green vehicle routing problem (GVRP) is scheduling vehicles to optimize both economic and environmental costs during the distribution process. This work presents heterogeneous GVRP with customers service restrictions (HGVRP-CSR), in which each customer is only allowed to be served by the designated vehicles. It aims at scheduling vehicles to minimize the economic cost of fuel consumption and CO2 emission. A rigorous mathematical program is constructed. Then, an enhanced ant colony optimization algorithm is proposed to solve it. The maximum and minimum pheromones are utilized to overcome premature convergence and guarantee population diversity. Furthermore, a variable neighborhood search (VNS) approach is adopted to execute systematic neighborhood search in the local search stage of ACO. Finally, extensive experiments are conducted and the results show that the proposed algorithm is an effective and efficient heuristics to solve HGVRP-CSR.