An Ameliorative Hybrid Algorithm for Solving the Capacitated Vehicle Routing Problem

One of the most applicable versions of the Vehicle Routing Problem (VRP) which has been widely studied in logistic services is Capacitated Vehicle Routing Problem (CVRP). There are many algorithms to solve the CVRP to minimize total travelled distance. Some of the most recent and efficient metaheuristic algorithms are capable of generating solutions within 0.5% to 1% gap from the optimum for instance problems adopted from the literature considering hundreds or thousands of demand points. In this contribution, a novel hybrid algorithm is proposed based on Gravitational Emulation Local Search (GELS) and Genetic Algorithm (GA). This algorithm alleviates the weaknesses of the GELS algorithm. The performance of the proposed algorithm, which is called GELSGA, is compared with other meta-heuristics. The obtained results show that the proposed algorithm can compete vigorously with them. In addition, the proposed algorithm could obtain solutions close to the Best Known Solutions (BKS) for many instance problems.

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