Parameter setting problem in the case of practical Vehicle Routing Problems with realistic constraints

• Autorzy: Žunić Emir , Đonko Dženana

Identyfikatory

DOI

10.15439/2019F194

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

customer service; decision making; graph theory; road vehicles; transportation; vehicle

PL

obsługa klienta; podejmowanie decyzji; teoria grafów; pojazdy drogowe; transport; pojazd

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 755--759
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., wykr.

Twórcy

autor

Žunić Emir

• Info Studio d.o.o. Sarajevo
• Faculty of Electrical Engineering, University of Sarajevo, Bosnia and Herzegovina

autor

Đonko Dženana

• Faculty of Electrical Engineering, University of Sarajevo, Bosnia and Herzegovina

Bibliografia

• 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

Uwagi

1. Track 4: Information Systems and Technology

2. Technical Session: 25th Conference on Knowledge Acquisition and Management

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).