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Problem statement: Vehicle routing problem determines the optimum route for each vehicle&#13; as a sequence of visiting cities. The problem has been defined as NP-hard and exact solution is relatively&#13; difficult to achieve for real time large scale models. Though several attempts to solve the problem were&#13; made in the literature, new approaches may be tried to solve the problem to further reduce computational&#13; efforts. Approach: In this context this study focuses on maximum utilization of loading capacity and&#13; determines the optimum set of vehicle routes for Capacitated Vehicle Routing Problem (CVRP) by a&#13; Nested Particle Swarm Optimization (NPSO) technique. The algorithm is implemented as Master PSO&#13; and slave PSO for the identification of candidate list and route sequence in nested form to optimize the&#13; model. Results: Benchmarking data set of capacitated vehicle routing is considered for the evaluations.&#13; The total distance of set vehicle route obtained by the new approach is compared with the best known&#13; solution and other existing techniques. Conclusions/Recommendations: The NPSO produces&#13; significant results and computational performance than the existing PSO algorithms. This newly proposed&#13; NPSO algorithm develops the vehicle schedule without any local optimization technique

Optimization of Capacitated Vehicle Routing Problem by Nested Particle Swarm Optimization