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Sampling-Based Genetic Algorithms for the Bi-Objective Stochastic Covering Tour Problem

  • Michaela Zehetner
  • Walter J. Gutjahr
Chapter
  • 669 Downloads
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)

Abstract

The paper investigates a sampling-based extension of the NSGA-II algorithm, applied to the solution of a bi-objective stochastic covering tour problem. The proposed extension uses variable samples for gradually improving approximations to the Pareto front. The approach is evaluated on a test benchmark for a humanitarian logistics application with data from Senegal. Comparisons to alternative solution techniques, in particular also to the exact solution of the deterministic counterpart problem based on a fixed sample, show the superiority of our approach.

Keywords

Humanitarian logistics Genetic algorithms Multi-objective optimization Stochastic optimization Covering tour problem 

Notes

Acknowledgements

We want to express our thanks to Fabien Tricoire for his help during the preparation of this paper.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Michaela Zehetner
    • 1
  • Walter J. Gutjahr
    • 1
  1. 1.University of ViennaWienAustria

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