This work presents the TASNOP-Teaching Algorithms for Solving Network

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This work presents a hybrid strategy for optimization of Capacitated Vehicle Routing Problem (CVRP) that employs Genetic Algorithms (GA) combined with the heuristics of Gillett & Miller (GM) and Hill Climbing (HC). The first heuristic is used to incorporate feasible solutions in the initial population of the GA while the second is responsible for the refinement of solutions after a certain number of generations without improvements. The computational experiments showed that the proposed strategy presented good results for the optimization of CVRP with respect to the quality of solutions well as the computational cost.

This work presents a hybrid approach called GA-NN for solving the Capacitated Vehicle Routing Problem (CVRP) using Genetic Algorithms (GA) and Nearest Neighbor heuristic (NN). The first technique was applied to determine the groups of customers to be served by the vehicles while the second is responsible to build the route of each vehicle. In addition, the heuristics of Gillett & Miller (GM) and Downhill (DH) were used, respectively, to generate the initial population of GA and to refine the solutions provided by GA. In the results section, we firstly present experiments demonstrating the performance of the NN heuristic for solving the Shortest Path and Traveling Salesman problems. The results obtained in such experiments constitute the main motivation for proposing the GA-NN. The second experimental study shows that the proposed hybrid approach achieved good solutions for instances of CVRP widely known in the literature, with low computational cost. It also allowed us to evidence that the use of GM and DH helped the hybrid GA-NN to converge on promising points in the search space, with a small number of generations.

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