Multi-objective evolutionary optimization of a military air transportation fleet mix with the flexibility objective

Author

Wojtaszek, Daniel ; Wesolkowski, Slawomir

Author_Institution

DRDC, Ottawa, ON, Canada

fYear

2011

fDate

11-15 April 2011

Firstpage

96

Lastpage

101

Abstract

The Non-dominated Sorting Genetic Algorithm-II is applied to a multi-objective air transportation fleet-mix problem for finding flexible fleet mixes. The Stochastic Fleet Estimation model, which is Monte Carlo-based, is used to determine average annual requirements that a fleet must meet. We search for Pareto-optimal combinations of platform-to-task assignments that can be used to complete stochastically generated scenarios. Solutions are evaluated using three objectives, with a goal of maximizing flexibility in accomplishing each task within its closure time, and minimizing fleet cost and total task duration. Optimization over all three objectives found very flexible low cost fleets, which were not discovered using previous two-objective and three-objective optimizations.

Keywords

Monte Carlo methods; Pareto analysis; genetic algorithms; military aircraft; stochastic processes; transportation; Monte Carlo-based model; Pareto-optimal combinations; flexibility objective; flexible fleet mixes; military air transportation; multiobjective evolutionary optimization; nondominated sorting genetic algorithm-II; platform-to-task assignments; stochastic fleet estimation model; Air transportation; Computational modeling; Correlation; Estimation; Genetic algorithms; Optimization; Sorting; fleet mix; flexibility; military air transportation; multi-objective optimization model; nondominated sorting genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Security and Defense Applications (CISDA), 2011 IEEE Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-9939-7

Type

conf

DOI

10.1109/CISDA.2011.5945946

Filename

5945946