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Multiobjective Evolutionary Optimization of a Transportation Fleet with a New Monetary Cost Function

Slawomir Wesolkowski, Ziad Sakr, Bruno Di Stefano and Anna Lawniczak

The 2010 Summer Computer Simulation Conference (SCSC 10)
Ottawa, Canada, July 11-14, 2010


Summary

We propose a new monetary cost function for civilian transportation applications such as couriering cargo, or transporting passengers using a commercial airline. 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 used to approximate a discrete event simulation. SaFE generates a vehicle fleet based on the set of requirements that the fleet is supposed to meet. 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 two objectives, with the goal of minimizing fleet cost, and total task duration time (a performance measure). We present optimization results and describe differences between current results and past research done on a related military problem. Finally, we show why using the risk formulation of not being able to accomplish future scenarios for military applications does not make sense for civilian applications.


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