Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Modeling and Optimization of Multi-echelon Transportation systems—a hybrid approach

,

DOI: http://dx.doi.org/10.15439/2017F80

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 1057–1064 ()

Full text

Abstract. The efficient and timely distribution of freight goods is critical for supporting the demands of modern urban areas. Optimum freight ensures the survival and development of urban areas. In the contemporary logistic there are two main distribution strategies: direct distribution and multi-echelon distribution. In the direct distribution, means of transport, starting from the main distribution center, bring their freight directly to the delivery points, while in the multi-echelon systems, freight is delivered from the main distribution center to the delivery points through intermediate points (local warehouses, satellites).

This study presents a concept and implementation of a integrated approach to modeling and optimization the Multi-Echelon Systems. In the proposed approach, two methods of constraint logic programming (CLP) and mathematical programming (MP) were integrated and hybridized. The proposed hybrid approach will be compared with classical mathematical programming on the same data sets (known benchmarks) for illustrative multi-echelon model - Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP).

References

  1. A. Schrijver, “Theory of Linear and Integer Programming”, John Wiley & sons, 1998A.
  2. S. Kumar, R. Panneerselvam, “Survey on the Vehicle Routing Problem and Its Variants”, Intelligent Information Management, 4, 2012, pp. 66-74.
  3. A. Verrijdt de Kok, “Distribution planning for a divergent n-echelon network without intermediate stocks under service restrictions”, International Journal of Production Economics, 38, 1995, pp. 225–243.
  4. G. Perboli, R. Tadei, D. Vigo, “The Two-Echelon Capacitated Vehicle Routing Problem: Models and Math-Based Heuristics”, Transportation Science, v 45, 2012, pp. 364-380.
  5. A. Ligeza, “Improving Efficiency in Constraint Logic Programming Through Constraint Modeling with Rules and Hypergraphs”, Federated Conference on Computer Science and Information Systems (FedCSIS), 2012, pp. 101-107.
  6. G. Bocewicz, Z. Banaszak, “Declarative approach to cyclic steady states space refinement: periodic processes scheduling”, International Journal of Advanced Manufacturing Technology, Vol. 67, Issue 1-4, 2013, pp. 137-155.
  7. K, Apt, M. Wallace, “Constraint Logic Programming using Eclipse”, Cambridge University Press, 2006.
  8. P. Sitek, J. Wikarek, “A Hybrid Programming Framework for Modeling and Solving Constraint Satisfaction and Optimization Problems” Scientific Programming, vol. 2016, Article ID 5102616, 2016. http://dx.doi.org/10.1155/2016/5102616.
  9. P. Sitek, J. Wikarek, “A hybrid framework for the modelling and optimisation of decision problems in sustainable supply chain management”, International Journal of Production Research, vol 53(21), 2015, pp 6611-6628, http://dx.doi.org/10.1080/00207543. 2015.1005762
  10. P. Sitek, “A hybrid approach to the two-echelon capacitated vehicle routing problem (2E-CVRP)”, Advances in Intelligent Systems and Computing, 267, 2014, pp. 251–263, http://dx.doi.org/10.1007/978-3-319-05353-0_25.
  11. J.N. Hooker, “Logic, Optimization, and Constraint Programming”, Journal of Computing, 14(4), 2002, pp. 295–321.
  12. A. Bockmayr, T. Kasper, “Branch-and-Infer, A Framework for Combining CP and IP”, Constraint and Integer Programming Operations Research/Computer Science Interfaces, Series, Volume 27, 2004, pp. 59-87.
  13. Ch. Allen, C. Creary, S Chatwin, “Introduction To Relational Databases And Sql Programming”, McGraw-Hill Osborne; Pap/Cdr edition, 2003, ISBN-10:0072229241,ISBN-13:978-0072229240
  14. J. Wikarek, :Implementation Aspects of Hybrid Solution Framework”, Recent Advancees in Automation, Robotics and Measuring Techniques vol 267, 2014, pp. 317-328. http://dx.doi.org/10.1007/978-3-319-05353-0_31
  15. T. Crainic, N. Ricciardi, G. Storchi, “Advanced freight transportation systems for congested urban areas”, Transportation Research, part C 12, 2004, pp. 119–137.
  16. N. Christofides, S. Elion, “An algorithm for the vehicle dispatching problem”, Operational Research Quarterly, 20, 1996, pp. 309–318.
  17. ORO Group Web-page, http://www.orgroup.polito.it/
  18. K. Grzybowska K., B. Gajšek, “Regional logistics information platform as a support for coordination of supply chain”, Highlights of Practical Applications of Scalable Multi-Agent Systems, The PAAMS Collection, J. Bajo et al. (Eds.), 2016, pp. 61-72, http://dx.doi.org/10.1007/978-3-319-39387-2_6
  19. P. Nielsen, Z. Michna, N.A.D Do, “An Empirical Investigation of Lead Time Distributions”, IFIP Advances in Information and Communication Technology, 438 (PART 1), 2014, pp. 435-442, http://dx.doi.org/https://doi.org/10.1007/978-3-662-44739-0_53.
  20. P. Nielsen, L. Jiang, N.G.M Rytter, G. Chen, “An investigation of forecast horizon and observation fit’s influence on an econometric rate forecast model in the liner shipping industry”, Maritime Policy and Management, 41 (7), 2014, pp. 667-682. http://dx.doi.org/10.1080/03088839.2014.960499
  21. S. Deniziak, T. Michno, P. Pieta, “IoT-Based Smart Monitoring System Using Automatic Shape Identification”, Advances in Intelligent Systems and Computing book series (AISC, volume 511), 2015, pp. 1-18, http://dx.doi.org/https://doi.org/10.1007/978-3-319-46535-7_1