Hierarchical Bayesian approach for improving weights for solving multi-objective route optimization problem
- Resource Type
- Authors
- Arindam Roy; Romit S Beed; Sunita Sarkar
- Source
- International Journal of Information Technology. 13:1331-1341
- Subject
- Mathematical optimization
Optimization problem
Computer Networks and Communications
Computer science
Applied Mathematics
Bayesian probability
Posterior probability
020206 networking & telecommunications
02 engineering and technology
Bayesian inference
Field (computer science)
Dirichlet distribution
Computer Science Applications
Data-driven
symbols.namesake
Computational Theory and Mathematics
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
symbols
020201 artificial intelligence & image processing
Multinomial distribution
Electrical and Electronic Engineering
Information Systems
- Language
- ISSN
- 2511-2112
2511-2104
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. Generation of weights using the proposed Bayesian methodology can be used to develop a bona-fide Bayesian posterior distribution for the optima, thus properly and coherently quantifying the uncertainty about the optima.