Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Pareto-Front Exploration with Uncertain Objectives
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A method for generating a well-distributed Pareto set in nonlinear multiobjective optimization
Journal of Computational and Applied Mathematics
A two-phase local search for the biobjective traveling salesman problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Self-adaptive population size adjustment for genetic algorithms
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Reducing emergency services arrival time by using vehicular communications and Evolution Strategies
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The development of communication technologies integrated in vehicles allows creating new protocols and applications to improve assistance in traffic accidents. Combining this technology with intelligent systems will permit to automate most of the decisions needed to generate the appropriate sanitary resource sets, thereby reducing the time from the occurrence of the accident to the stabilization and hospitalization of the injured passengers. However, generating the optimal allocation of sanitary resources is not an easy task, since there are several objectives that are mutually exclusive, such as assistance improvement, cost reduction, and balanced resource usage. In this paper, we propose a novel approach for the sanitary resources allocation in traffic accidents. Our approach is based on the use of multi-objective genetic algorithms, and it is able to generate a list of optimal solutions accounting for the most representative factors. The inputs to our model are: (i) the accident notification, which is obtained through vehicular communication systems, and (ii) the severity estimation for the accident, achieved through data mining. We evaluate our approach under a set of vehicular scenarios, and the results show that a memetic version of the NSGA-II algorithm was the most effective method at locating the optimal resource set, while maintaining enough variability in the solutions to allow applying different resource allocation policies.