论文标题
分层贝叶斯的方法，用于改善权重解决多目标路线优化问题
Hierarchical Bayesian Approach for Improving Weights for Solving Multi-Objective Route Optimization Problem
论文作者
论文摘要
加权总和方法是一种简单且广泛使用的技术，它标明了多个冲突目标，成为一个单个目标函数。它遇到了确定与目标相对应的适当权重的问题。本文提出了一种基于多项式分布和Dirichlet的新型层次贝叶斯模型，然后才能完善权重解决此类多目标路线优化问题。该模型和方法围绕从小型试点调查获得的数据展开。该方法旨在改善智能运输系统领域中现有的重量确定方法，因为数据驱动通过适当的概率建模选择权重的选择可确保与非稳态技术相比，平均可靠的结果可靠。该模型和方法在模拟以及实际数据集中的应用表明，在稳定权重估计方面的表现非常令人鼓舞。
The weighted sum method is a simple and widely used technique that scalarizes multiple conflicting objectives into a single objective function. It suffers from the problem of determining the appropriate weights corresponding to the objectives. This paper proposes a novel Hierarchical Bayesian model based on Multinomial distribution and Dirichlet prior to refine the weights for solving such multi-objective route optimization problems. The model and methodologies revolve around data obtained from a small scale pilot survey. The method aims at improving the existing methods of weight determination in the field of Intelligent Transport Systems as data driven choice of weights through appropriate probabilistic modelling ensures, on an average, much reliable results than non-probabilistic techniques. Application of this model and methodologies to simulated as well as real data sets revealed quite encouraging performances with respect to stabilizing the estimates of weights.
