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Multi Objective Genetic Approach for Solving Vehicle Routing Problem

机译:多目标遗传算法求解车辆路径问题

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Vehicle Routing Problem (VRP) is a NP-Complete and a multi-objective problem. The problem involves optimizing a fleet of vehicles that are to serve a number of customers from a central depot. Each vehicle has limited capacity and each customer has a certain demand. Genetic Algorithm (GA) maintains a population of solutions by means of a crossover and mutation operators. For crossover and mutation best cost route crossover techniques and swap mutation procedure is used respectively. In this paper, we focus on two objectives of VRP i.e. number of vehicles and total cost (distance). The proposed Multi Objective Genetic Algorithm (MOGA) finds optimum solutions effectively.
机译:车辆路径问题(VRP)是一个NP完全问题,是一个多目标问题。问题涉及优化车队,以服务于来自中央仓库的许多客户。每辆车的容量有限,每位客户都有一定的需求。遗传算法(GA)通过交叉算子和变异算子维护大量的解决方案。对于交叉和突变,最佳成本路由交叉技术和交换突变过程分别被使用。在本文中,我们专注于VRP的两个目标,即车辆数量和总成本(距离)。提出的多目标遗传算法(MOGA)可以有效地找到最佳解决方案。

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