Open Access
Issue
MATEC Web Conf.
Volume 252, 2019
III International Conference of Computational Methods in Engineering Science (CMES’18)
Article Number 03003
Number of page(s) 6
Section Computational Artificial Intelligence
DOI https://doi.org/10.1051/matecconf/201925203003
Published online 14 January 2019
  1. N. Azi, M. Gendreau, J. Y. Potvin, A dynamic vehicle routing problem with multiple delivery routes Annals of Operations Research, 199(1), 103-112 (2012) [CrossRef] [Google Scholar]
  2. R. W. Bent, P. Van Hentenryck, Scenario-based planning for partially dynamic vehicle routing with stochastic customers Operations Research, 52(6), 977-987 (2004) [CrossRef] [Google Scholar]
  3. M. Caramia, G. Italiano, G. Oriolo A. Pacifici, A. Perugia, Routing a fleet of vehicles for dynamic combined pick-up and deliveries services Operations Research Proceedings 2001 (pp. 3-8). Springer, Berlin, Heidelberg (2002) [CrossRef] [Google Scholar]
  4. E. Dudek-Dyduch, Formalization and Analysis of Problems of Discrete Manufacturing Processes . Scientific Bulletin of AGH University, Automatics Volume 54, Krakow, Poland (1990); (In Polish) [Google Scholar]
  5. E. Dudek-Dyduch, Learning based algorithm in scheduling. J. Intell. Manuf., 11, 135–143 (2000) [CrossRef] [Google Scholar]
  6. E. Dudek-Dyduch, T. Dyduch, Learning algorithms for scheduling using knowledge based model Artif. Intell. Soft Comput.–ICAISC, 1091–1100 (2006) [Google Scholar]
  7. E. Dudek-Dyduch, Algebraic Logical Meta-Model of Decision Processes—New Metaheuristics In proceedings of International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, 14–18 June 2015, Lecture Notes in Computer Science, Springer Verlag, vol. 9119, 541–554 (2015) [CrossRef] [Google Scholar]
  8. E. Dudek-Dyduch, E. Kucharska, Learning Method for Co-operation. In proceedings of International Conference on Computational Collective Intelligence, Gdynia, Poland 21–23 September 2011., Lecture Notes in Computer Science, Springer-Verlag, 6923, 290–300 (2011) [CrossRef] [Google Scholar]
  9. L. Dutkiewicz, E. Kucharska, K. Raczka, GroblerK. Dębska, ST method-based algorithm for the supply routes for multilocation companies problem In Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Springer, Cham. 123-135 (2016) [CrossRef] [Google Scholar]
  10. J. Euchi, A. Yassine, H. Chabchoub, The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach, Swarm and Evolutionary Computation, 41-53, (2015) [CrossRef] [Google Scholar]
  11. K. Jobczyk, P. Wiśniewski, A. Ligęza Temporal Traveling Salesman Problem–in a Logic-and Graph Theory-Based Depiction In International Conference on Artificial Intelligence and Soft Computing, Springer, Cham. 544-556 (2018) [CrossRef] [Google Scholar]
  12. M. R. Khouadjia, B. Sarasola, El-G. Talbi, L. Jourdan, Metaheuristics for Dynamic Vehicle Routing In E. Alba, A. Nakib, P. Siarry ed. Metaheuristics for Dynamic Optimization, Springer Berlin Heidelberg”, 265-289 (2013) [CrossRef] [Google Scholar]
  13. R. Klimek, L. Kotulski, Towards a better understanding and behavior recognition of inhabitants in smart cities. A public transport case In proceedings of International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, 14–18 June 2015, Lecture Notes in Computer Science, Springer Verlag, vol. 9120, 237–246 (2015) [CrossRef] [Google Scholar]
  14. E. Kucharska, Heuristic Method for Decision-Making in Common Scheduling Problems Applied Sciences, 7(10), 1073 (2017). [CrossRef] [Google Scholar]
  15. E. Kucharska, K. Grobler-Dębska, K. Rączka, Algebraic-logical meta-model based approach for scheduling manufacturing problem with defects removal Advances in Mechanical Engineering, 9(4), 1687814017692291. (2017) [CrossRef] [Google Scholar]
  16. R.J. Kuo, B.S. Wibowo, F.E. Zulvia Application of a fuzzy ant colony system to solve the dynamic vehicle routing problem with uncertain service time, Applied Mathematical Modelling, 9990 – 10001, (2016). [CrossRef] [Google Scholar]
  17. C. Novoa, R. Storer, An approximate dynamic programming approach for the vehicle routing problem with stochastic demands European Journal of Operational Research, 196(2), 509-515 (2009) [CrossRef] [Google Scholar]
  18. R. Montemanni, L. M. Gambardella, A. E. Rizzoli, V. V. Donati, A. Ant colony system for a dynamic vehicle routing problem Journal of Combinatorial Optimization, 10(4), 327-343 (2005) [CrossRef] [Google Scholar]
  19. V. Pillac, M. Gendreau, C. Guéret, A. L. Medaglia, A review of dynamic vehicle routing problems European Journal of Operational Research, 225(1), 1-11 (2013) [CrossRef] [Google Scholar]
  20. J. Yang, P. Jaillet, H. Mahmassani, Real-time multivehicle truckload pickup and delivery problems Transportation Science, 38(2), 135-148 (2004) [CrossRef] [Google Scholar]
  21. X. Yangkun, F. Zhuo, Improved tabu search algorithm for the open vehicle routing problem with soft time windows and satisfaction rate, Cluster Computing, (2018) [Google Scholar]
  22. P. Wiśniewski, K. Kluza and A. Ligeza, Decision support system for robust urban transport management, Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, pp. 1069-1074. (2017) [Google Scholar]

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