Solving CVRP with time window, fuzzy travel time and demand via a hybrid ant colony optimization and genetic algortihm

This study intends to propose a hybrid ant colony optimization (ACO) and genetic algorithm (GA) (HACOGA) for solving the capacitated vehicle routing problem (CVRP) with time window, fuzzy travel time and demand. A mathematical model for CVRP with time window, fuzzy travel time and demand is first constructed. It applies fuzzy credibility and ranking approaches. Then, the proposed HACOGA which combines ACO with GA to accelerate its exploration is employed. It also embeds local search algorithms to generate a better initial solution and improve its performance at the end of evolution. The proposed algorithm is verified using an instance of CVRP with time window and fuzzy travel time first. The simulation result indicates that the proposed HACOGA outperforms previous methods. Furthermore, a simulation example is employed to show the effectiveness of the proposed algorithm for solving CVRP with time window, fuzzy travel time and fuzzy demand. The computational results reveal that HACOGA still has the best performance.