Bayesian Hierarchical Multi-Objective Optimization for Vehicle Parking Route Discovery
This addresses the issue of time and fuel wastage for drivers during peak hours, but it is incremental as it builds on existing multi-objective optimization methods.
The paper tackles the problem of finding optimal routes to parking lots by proposing a Bayesian hierarchical technique for multi-objective optimization, using a probabilistic method to refine weights and genetic algorithms, with simulated data showing routes that closely match real-life situations.
Discovering an optimal route to the most feasible parking lot has been a matter of concern for any driver which aggravates further during peak hours of the day and at congested places leading to considerable wastage of time and fuel. This paper proposes a Bayesian hierarchical technique for obtaining the most optimal route to a parking lot. The route selection is based on conflicting objectives and hence the problem belongs to the domain of multi-objective optimization. A probabilistic data driven method has been used to overcome the inherent problem of weight selection in the popular weighted sum technique. The weights of these conflicting objectives have been refined using a Bayesian hierarchical model based on Multinomial and Dirichlet prior. Genetic algorithm has been used to obtain optimal solutions. Simulated data has been used to obtain routes which are in close agreement with real life situations.