Table of Contents
Journal of Industrial Engineering
Volume 2017, Article ID 3019523, 13 pages
Research Article

Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

Department of Industrial Management, University of Kelaniya, Kelaniya, Sri Lanka

Correspondence should be addressed to Thashika D. Rupasinghe;

Received 6 September 2016; Revised 17 December 2016; Accepted 21 December 2016; Published 18 January 2017

Academic Editor: Shu-Chu Liu

Copyright © 2017 P. L. N. U. Cooray and Thashika D. Rupasinghe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. I. Kara, B. Y. Kara, and M. Kadri Yetis, “Energy minimizing vehicle routing problem,” in Combinatorial Optimization and Applications, vol. 4616 of Lecture Notes in Computer Science, pp. 62–71, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  2. M. Gendreau, “Metaheuristics in vehicle routing,” in Proceedings of the International Conference on Operations Research and Enterprise Systems (ICORES '12), 2012.
  3. M. Birattari, Tuning Metaheuristics: A Machine Learning Perspective, vol. 197, Springer, Berlin, Germany, 2009.
  4. M. Gendreau, J. Y. Potvin, O. Bräumlaysy, G. Hasle, and A. Løkketangen, “Metaheuristics for the vehicle routing problem and its extensions: a categorized bibliography,” in The Vehicle Routing Problem: Latest Advances and New Challenges, pp. 143–169, Springer US, 2008. View at Google Scholar
  5. C. Lin, K. L. Choy, G. T. S. Ho, S. H. Chung, and H. Y. Lam, “Survey of green vehicle routing problem: past and future trends,” Expert Systems with Applications, vol. 41, no. 4, pp. 1118–1138, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. D. M. Yazan, A. M. Petruzzelli, and V. Albino, “Analyzing the environmental impact of transportation in reengineered supply chains: a case study of a leather upholstery company,” Transportation Research Part D: Transport and Environment, vol. 16, no. 4, pp. 335–340, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Björklund, “Influence from the business environment on environmental purchasing—drivers and hinders of purchasing green transportation services,” Journal of Purchasing and Supply Management, vol. 17, no. 1, pp. 11–22, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. I. T. F. Leipzig, Reducing Transport Greenhouse Gas Emissions: Trends & Data, Background for the 2010 International Transport Forum, Berlin, Germany, 2010.
  9. J. K. Lenstra and A. H. G. R. Kan, “Complexity of vehicle routing and scheduling problems,” Networks, vol. 11, no. 2, pp. 221–227, 1981. View at Publisher · View at Google Scholar · View at Scopus
  10. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductoryanalysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, Mich, USA, 1975. View at MathSciNet
  11. F. Dobslaw, “A parameter tuning framework for metaheuristics based on design of experiments and artificial neural networks,” in Proceedings of the International Conference on Computer Mathematics and Natural Computing (WASET '10), 2010.
  12. P. L. N. U. Cooray and T. D. Rupasinghe, “An analysis of methodologies for solving green vehicle routing problem: a systematic review of literature,” in Proceedings of the Conference on Research for Transport and Logistics Industry, Colombo, Sri Lanka, 2016.
  13. E. Uchoa, D. Pecin, A. Pessoa, M. Poggi, A. Subramanian, and T. Vidal, “New benchmark instances for the capacitated vehicle routing problem,” Research Report Engenharia de Produção, Universidade Federal Fluminense, 2014. View at Google Scholar