Hierarchical Bayesian Approach for Improving Weights for Solving Multi-Objective Route Optimization Problem
This work addresses a specific bottleneck in Intelligent Transport Systems by providing a data-driven probabilistic approach for weight determination, representing an incremental improvement over existing methods.
The paper tackled the problem of determining appropriate weights for the weighted sum method in multi-objective route optimization by proposing a Hierarchical Bayesian model based on Multinomial distribution and Dirichlet prior. The result showed encouraging performance in stabilizing weight estimates when applied to simulated and real datasets.
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.