DocumentCode :
2330036
Title :
Multiobjective evolutionary algorithm with risk minimization applied to a fleet mix problem
Author :
Willick, Kyle ; Wesolkowski, Slawomir ; Mazurek, Michael
Author_Institution :
Canadian Forces Aerosp. Warfare Centre OR Team in Ottawa, Ottawa, ON, Canada
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
7
Abstract :
We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to a multi-objective fleet-mix problem for risk mitigation. The Stochastic Fleet Estimation (SaFE) model, a Monte Carlo-based model, is used to determine average annual requirements which a fleet must meet. We search for Pareto-optimal combinations of platform-to-task assignments that can be used to complete SaFE generated scenarios. Solutions are evaluated using three objectives, with a goal of minimizing fleet cost, total task duration, and the risk that a solution will not be able to accomplish future scenarios. Optimization over all three objectives allowed for exploration of configurations which were low cost and low risk, a region not explored by prior experiments without the risk objective.
Keywords :
Monte Carlo methods; Pareto optimisation; evolutionary computation; genetic algorithms; risk analysis; stochastic processes; transportation; Monte Carlo based model; Pareto optimal combination; fleet mix problem; multiobjective evolutionary algorithm; nondominated sorting genetic algorithm-II; platform to task assignment; risk minimization; stochastic fleet estimation; Approximation methods; Computational modeling; Equations; Estimation; Mathematical model; Optimization; Probability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2010 IEEE Congress on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6909-3
Type :
conf
DOI :
10.1109/CEC.2010.5586273
Filename :
5586273
Link To Document :
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