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Journal: International Journal of Advanced Research (Vol.9, No. 03)

Publication Date:

Authors : ; ;

Page : 59-64

Keywords : Grasshopper Optimization Algorithm Vehicle Routing Problem Simulated Annealing Algorithm;

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This paper is focused on the study of the basic problem of the vehicle for reducing the cost factor and increasing efficiency of the solution. Features and constraint uses some capabilities of the algorithm used to modify it dynamically between the nodes and depot. This is demonstrated with a feasible schedule for every node and minimizes the total cost as much as possible. The analysis is based on the address of the given model and solution procedure.The purpose of this research paper is to provide examples of models and applications which include the profits, extensions and partitioned features. The objective is to minimize the traveled distance that visits every subset of nodes one after another while maximizing or satisfying a minimum collected profit from each visited node. The concepts of VRP are discussed in Section I and the issues discussed in paper are in Section VI. Section VI also contains the modeling aspects and constraints that can be used in solving VRP in this paper. Simulated annealing and grasshopper optimization algorithm are combined for solving vehicle routing problem as discussed in Section VII. This study investigates both the variants of algorithm for the clustering nodes and different methods for the generation of routes to overcome optimal VRP solution. In conventional grasshopper algorithm, shortest path for certain node that starts from center depot is calculated by means of local search algorithms. Few methods such as ant colony optimization and genetic algorithm are considered for the route optimization. We can compare the performance of these methods to solve the VRP. Therefore, performance of the proposed method is able to produce better solutions than the other methods which reveal a large number of benchmark experimental results and is very promising.

Last modified: 2021-04-03 15:36:17