Multimodal Transportation p-hub Location Routing Problem with Simultaneous Pick-ups and Deliveries

Authors

1 MSc, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Professor, Faculty of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Centralizing and using proper transportation facilities cut down costs and traffic. Hub facilities concentrate on flows to cause economic advantage of scale and multimodal transportation helps use the advantage of another transporter. A distinctive feature of this paper is proposing a new mathematical formulation for a three-stage p-hub location routing problem with simultaneous pick-ups and deliveries on time. A few studies have been devoted to this problem; however, many people are still suffering from the problems of commuting in crowded cities. The proposed formulation controlled the tumult of each node by indirect fixed cost. Node-to-node traveling cost was followed by a vehicle routing problem between nodes of each hub. A couple of datasets were solved for small and medium scales by GAMS software. But, for large-scale instances, a meta-heuristic algorithm was proposed. To validate the model, datasets were used and the results demonstrated the performance suitability of the proposed algorithm.

Keywords


Alumur, S. A., Kara, B. Y., & Yaman, H. (2012a). Hierarchical multimodal hub location problem with time definite deliveries. Transportation Research Part E 48, 1107-1120.

Alumur, S., Kara, B. Y., & Karasan, O. E. (2012b). Multimodal hub location and hub network design. OMEGA 40, 927-939.

Alumur, S; Kara, B Y. (2008). Invented review network hub location problems: The state of the art. European journal of operational research 190, 1-21.

Camargo, R. S., Miranda, G. D., & Løkketangen, A. (2013). A new formulation and an exact approach for the many-to-many hub location routing problem. Applied Mathematical Modeling 37, 7465-7480.

Eskigun, E., RehaUzsoy, Preckel, P. V., Beaujon, G., Krishnan, S., & Tew, J. D. (2005). Outbound supply chain network design with mode selection, lead times and capacitatedvehicle distribution centers. European Journal of Operational Research 165, 182–206.

Gelareh, S., Maculan, Philip, M. N., & NematianMonemi, R. (2013). Hub and spoke network design and fleet deployment for string planning of liner shipping. Applied mathematical modeling 37, 3307-3321.

Gupta, R., & Pirkul, H. (2000). Theory and Methodology Hybrid fiber co-axial CATV network design with variable capacity optical network units. European Journal of Operational Research 123, 73-85.

Hayuth, Y. (1987). Intermodality: Concept and Practice. London: Lloyds of London Press.

Julai, F., Razmi, J., & Rostami, N. K. (2011). A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem. Central European journal of operation research 19(4), 547-569.

Norouzi, N., Razmi, J., & Amalnik, S. (2012). Consume optimization of a vehicle routing problem with IPSO algorithm. University of Tehran journal of industrial engineering 47, 105-112.

Rabbani, M., Zameni, S., & Kazemi, S. M. (2013). Proposing a new mathematical formulation for modeling costs in a p-hub center problem. ICMSAO. Tunisia: IEEE.

Razmi, J., & Rahmanniya, F. (2013). Design of distribution network using hub location model with regard to capacity constraint and service level. International Journal of Logistics Systems and Management 16, 386-398.

Tancrez, J. S., lange, J. C., & Semal, P. (2012). A location-inventory model for large three-level supply chains. Transportation research part E 48, 485-502.

Van Schijndel, W. J., & Dinwoodie, J. (2000). Congestion and multimodal transport: a survey of cargo transport operators in the Netherlands. Transport Policy 7, 231-241.

Zhang, J., Liao, F., Arentze, T., & Timmermans, H. (2011). A multimodal transport network model for advanced traveler information systems. Procedia Computer Science 5, 912–919.

Zhi-Hua, H. (2011). A container multimodal transportation scheduling approach based on immune affinity model for emergency relief. Expert Systems with Applications 38, 2632–2639.