Abstract
The efficient and timely distribution of freight is critical for supporting the demands of modern urban areas. Without optimal freight distribution, urban areas could not survive and develop. The paper presents the concepts of hybrid approach to optimization of urban freight distribution. This approach proposed combines the strengths of mathematical programming (MP) and constraint logic programming (CLP), which leads to a significant reduction in the search time necessary to find the optimal solution and allows solving larger problems. It also presents the formal model for optimization of urban freight distribution with different types of time constraints. The application of the hybrid approach to the optimization of urban freight distribution is the primary contribution of this paper. The proposed model was implemented using both the hybrid approach and pure mathematical programming for comparison. Several experiments were performed for both computational implementations in order to evaluate both approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
1. Russo F., Comi A., Polimeni A. Urban freight transport and logistics: Retailer’s choices. In: Innovations in City Logistics (E. Taniguchi And R. G. Thompson eds.), Nova Science Publishers, Hauppauge Ny (USA), 2008, ISBN 978-1-60456-725-0
2. DG MOVE European Commission: Study on Urban Freight Transport FINAL REPORT By MDS Transmodal Limited in association with Centro di ricerca per il Trasporto e la Logistica (CTL), 2012.
3. Sitek, P., Wikarek, J. A Hybrid Approach to the Optimization of Multiechelon Systems, Mathematical Problems in Engineering, Article ID 925675, Hindawi Publishing Corporation, 2014, DOI:10.1155/2014/925675.
4. Sitek P., Wikarek J. A Hybrid Programming Framework for Modeling and Solving Constraint Satisfaction and Optimization Problems. Scientific Programming, vol. 2016, Article ID 5102616, 2016. doi:10.1155/2016/5102616.
5. Schrijver, A. Theory of Linear and Integer Programming. John Wiley &Sons, New York, NY, USA, 1998.
6. Apt, K., Wallace, M. Constraint Logic Programming using Eclipse. Cambridge: Cambridge University Press, 2006.
7. Rossi, F., Van Beek, P., Walsh, T. Handbook of Constraint Programming (Foundations of Artificial Intelligence). New York: Elsevier Science Inc, 2006.
8. Bocewicz G., Nielsen I., Banaszak Z. Iterative multimodal processes scheduling. Annual Reviews in Control, 38(1), 2014, 113–132.
9. Wikarek, J. Implementation aspects of Hybrid Solution Framework (HSF). Recent Advances in Automation, Robotics and Measuring Techniques Advances in Intelligent Systems and Computing, 267, 2014, 317–328, DOI: 10.1007/978-3-319-05353-0_31
10. Sitek P. A hybrid approach to the two-echelon capacitated vehicle routing problem (2E-CVRP). Advances in Intelligent Systems and Computing, 267, 2014, 251–263, DOI: 10.1007/978-3-319-05353-0_25.
11. Hooker J. N. Logic, optimization, and constraint programming. INFORMS Journal on Computing, vol. 14, no. 4, 2002, 295–321.
12. Bockmayr, A., Kasper, T. A Framework for Combining CP and IP, Branch-and-Infer, Constraint and Integer Programming: Toward a Unified Methodology Operations Research/Computer Science Interfaces, 27, 2014, 59–87.
13. Milano, M., Wallace, M. Integrating Operations Research in Constraint Programming. Annals of Operations Research, 175(1), 2010, 37–76.
14. Seuring, S., Müller, M. From a Literature Review to a Conceptual Framework for Sustain-able Supply Chain Management. Journal of Cleaner Production 16, 2008, 1699–1710.
15. Kłosowski G., Gola A., Świć A. Application of Fuzzy Logic in Assigning Workers to Pro-duction Tasks. Distributed Computing and Artificial Intelligence, 13th International Conference, AISC, Vol. 474, 2016, 505–513, DOI: 10.1007/978-3-319-40162-1_54.
16. Relich, M. Identifying Project Alternatives with the Use of Constraint Programming. Borzemski, L. et al. (eds.), Information Systems Architecture and Technology, Advances in Intelligent Systems and Computing, vol. 521, Springer, 2017, 3–13.
17. Grzybowska K., Łupicka A. Knowledge Acquisition in Complex Systems. Proceedings of the 2016 International Conference on Economics and Management Innovations, part of Advances in Computer Science Research, vol 57, Yue X.-G., Duarte N.J.R. (eds.), 2016, 262–266, DOI: 10.2991/icemi-16.2016.5
18. Nielsen P., Nielsen I., Steger-Jensen K. Analyzing and evaluating product demand inter-dependencies. Computers in Industry, 61 (9), 2010, 869–876,. doi:10.1016/j.compind.2010.07.012.
19. Krenczyk, D. Jagodzinski, J. ERP, APS and Simulation Systems Integration to Support Production Planning and Scheduling. Advances in Intelligent Systems and Computing, Vol. 368, Springer International Publishing, 2015, 451–46,
20. Bak S., Czarnecki R., Deniziak S., Synthesis of Real-Time Cloud Applications for Internet of Things. Turkish Journal of Electrical Engineering &Computer Sciences, 2013. doi:10.3906/elk-1302-178.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Sitek, P., Wikarek, J., Stefański, T. (2018). Optimization of urban freight distribution with different time constraints - a hybrid approach. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds) Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-62410-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-62410-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62409-9
Online ISBN: 978-3-319-62410-5
eBook Packages: EngineeringEngineering (R0)