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首页> 外文期刊>International journal of advanced intelligence paradigms >Adaptive probabilities of crossover and mutation in genetic algorithm for solving stochastic vehicle routing problem
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Adaptive probabilities of crossover and mutation in genetic algorithm for solving stochastic vehicle routing problem

机译：求解随机车辆路径问题的遗传算法中交叉和变异的自适应概率

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摘要

The vehicle routing problem (vehicle routing problem, VRP in remainder of this paper) is a combinatorial optimisation problem and operational research. It belongs to the category of transportation problems, as the travelling salesman problem (travelling salesman problem, TSP) and the chance-constrained programming (CCP). These problems in the field of logistics, one or more vehicles must cover transportation network to deliver goods to customers or cover the roads network. Solving the problem is to determine a set of tours that minimise the best targets as the total distance travelled, the number of vehicles used, the sum of the delays of customers, i.e. This article describes a new algorithm for solving transportation problems with modified boundary conditions to minimise the uncertainty in the travel parameters, where a gain is associated with each customer and where the objective is to maximise the total gain collected and minimise the routing costs.
机译：车辆路径问题（车辆路径问题，本文其余部分中的VRP）是组合优化问题和运筹学。它属于运输问题的类别，如旅行商问题（旅行商问题，TSP）和机会受限编程（CCP）。在物流领域中的这些问题，一辆或多辆车辆必须覆盖运输网络才能向客户交付货物或覆盖道路网络。解决问题的方法是确定一组行程，该行程将最佳目标减至最小，例如总行驶距离，使用的车辆数量，客户延误的总和，即本文介绍了一种用于解决边界条件已修改的运输问题的新算法最小化行程参数的不确定性，其中每个客户都有收益，目标是最大化收集的总收益并最小化路线成本。

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