Minimizing risk on a fleet mix problem with a multiobjective evolutionary algorithm

Author

Mazurek, Michael ; Wesolkowski, Slawomir

Author_Institution

OR Team, Canadian Forces Aerosp. Warfare Centre, Ottawa, ON, Canada

fYear

2009

fDate

8-10 July 2009

Firstpage

1

Lastpage

7

Abstract

We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to perform a multiobjective optimization of the stochastic fleet estimation (SaFE) model. SaFE is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. We search for Pareto-optimal combinations of valid platform-assignments for a list of tasks, which can be applied to complete scenarios output by SaFE. Solutions are evaluated on three objectives, with the goal of minimizing fleet cost, total task duration time, and the risk that a solution will not be able to accomplish possible future scenarios.

Keywords

Monte Carlo methods; Pareto optimisation; genetic algorithms; military vehicles; risk management; transportation; Monte Carlo-based model; NSGA-II; Pareto-optimal; SaFE model; fleet mix problem; military; multiobjective evolutionary algorithm; multiobjective optimization; nondominated sorting genetic algorithm-II; risk minimization; stochastic fleet estimation; total task duration time; vehicle fleet; Computational intelligence; Cost function; Evolutionary computation; Frequency; Organizations; Security; Stochastic processes; Testing; Uncertainty; Vehicle safety;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009. IEEE Symposium on

Conference_Location

Ottawa, ON

Print_ISBN

978-1-4244-3763-4

Electronic_ISBN

978-1-4244-3764-1

Type

conf

DOI

10.1109/CISDA.2009.5356525

Filename

5356525