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AN APPROACH FOR VEHICLE ROUTING PROBLEM USING GRASSHOPPER OPTIMIZATION ALGORITHM AND SIMULATED ANNEALING

车辆路径问题 数学优化 模拟退火 蚁群优化算法 计算机科学 聚类分析 节点(物理) 遗传算法 最短路径问题 地铁列车时刻表 算法 布线(电子设计自动化) 数学 工程类 图形 人工智能 理论计算机科学 操作系统 结构工程 计算机网络
作者
Sunil Boro,Santosh Kumar Behera
出处
期刊:International journal of advanced research [International Journal Of Advanced Research]
卷期号:9 (03): 59-64 被引量:1
标识
DOI:10.21474/ijar01/12554
摘要

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.

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