Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Parameter Setting Problem in the Case of Practical Vehicle Routing Problems with Realistic Constraints

,

DOI: http://dx.doi.org/10.15439/2019F194

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 755–759 ()

Full text

Abstract. Vehicle Routing Problem (VRP) is the process of selection of the most favorable roads in a road network vehicle should move during the customer service, so as such, it is a generalization of problems of a commercial traveler. Most of the algorithms for successful solution of VRP problems are consisted of several controll parameters and constants, so this paper presents the data-driven prediction model for adjustment of the parameters based on historical data, especially for practical VRP problems with realistic constraints. The approach is consisted of four prediction models and decision making systems for comparing acquired results each of the used models.

References

  1. Dantzig, G. B., Ramser, J. H. 1959. The truck dispatching problem. Management science. 6(1):80-91, https://doi.org/10.1287/mnsc.6.1.80
  2. Lee, W. L. 2013. Real-Life Vehicle Routing with Non-Standard Constraints. Proceedings of the World Congress on Engineering (WCE). I:432-437
  3. Hu, X., Huang, M., Zeng, A. 2007. An intelligent solution system for a vehicle routing problem in urban distribution. International Journal of Innovative Computing, Information and Control. 3:189-198
  4. Musolino, G., Rindone, C., Polimeni, A., Vitetta, A. 2013. Travel Time Forecasting and Dynamic Routes Design for Emergency Vehicles. Procedia - Social and Behavioral Sciences. 87:193-202, https://doi.org/10.1016/j.sbspro.2013.10.603
  5. Fu, C., Wang, H. 2010. The solving strategy for the real-world vehicle routing problem. 3rd International Congress on Image and Signal Processing. 3182-3185, https://doi.org/10.1109/CISP.2010.5647968
  6. Calvet, L., Juan, A. A., Serrat, C., Ries, J. 2016. A statistical learning based approach for parameter fine-tuning of metaheuristics. SORT - Statistics and Operations Research Transactions. 40(1):201-240
  7. Birattari, M., Kacprzyk, J. 2009. Tuning metaheuristics: A machine learning perspective. Springer, Vol. 197. ISBN: 3642004822 9783642004827
  8. Montero, E., Riff, M. C., Neveu, B. 2014. A beginner’s guide to tuning methods. Applied Soft Computing. 17:39-51, https://doi.org/10.1016/j.asoc.2013.12.017
  9. Ries, J., Beullens, P., Salt, D. 2012. Instance-specific multi-objective parameter tuning based on fuzzy logic. EJOR. Elsevier. 218:305-315, https://doi.org/10.1016/j.ejor.2011.10.024
  10. Battiti, R., Brunato, M. 2010. Reactive Search Optimization: Learning While Optimizing. Handbook of Metaheuristics. International Series in Operations Research & Management Science. 543-571, https://doi.org/10.1007/978-1-4419-1665-5_18
  11. Žunić, E., Hindija, H., Beširević, A., Hodžić, K., Delalić, S. 2018. Improving Performance of Vehicle Routing Algorithms using GPS Data. 14th Symposium on Neural Networks and Applications (NEUREL). 1-4. https://doi.org/10.1109/neurel.2018.8586982
  12. Žunić, E., Djedović, A., Đonko, D. 2017. Cluster-based analysis and time-series prediction model for reducing the number of traffic accidents. International Symposium ELMAR. 25-29, https://doi.org/10.23919/ELMAR.2017.8124427
  13. Žunić, E. (Emir). 2018. Real-world VRP data with realistic non-standard constraints - parameter setting problem regression input data. 4TU.Centre for Research Data. Dataset. Available at: https://doi.org/10.4121/uuid:97006624-d6a3-4a29-bffa-e8daf60699d8