BENDERS DECOMPOSITION TO SOLVE LARGE-SCALE OF THE FACILITY LOCATION PROBLEM IN TRANSPORTATION

Dewi Hajar; Bachtiar Herdianto

Authors

Dewi Hajar Politeknik Multimedia Nusantara
Bachtiar Herdianto Politeknik Multimedia Nusantara

Keywords:

transportation problem, facility location problem

Abstract

In supply chain management and logistics systems, the transportation costs often represent an important part. The design of transportation network offers a great potential to reduce costs, time as well as improve service quality. Hence, determining the efficient solution for large-scale of transportation problems is an important task in the field of operations research, where the problem can be formulated as the Facility Location problem (FLP). The FLP seeks to locate a number of facilities to serve a number of customers. Systematic approach to the FLP have been studied in the operations research literature, yet the best possible result in rapid computational time is still unknown. Meanwhile, Benders decomposition is an exact algorithm that allows the solution of very large linear programming problems, quickly and optimally. In this research, we consider the Capacitated Facility Location Problem (CFLP). We seek to address the facility location strategy such that the location of hubs, the allocation of supplier/client nodes to hubs, as well as the inter-hub freight transportations, in order to achieve an efficient network design system. The main goal of the model is to find the global optimal solution of large-scale problem in reasonable computation time.

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References

M. Albareda-sambola, “Location Science,” Locat. Sci., pp. 399–418, 2015, doi: 10.1007/978-3-319-13111-5.

J. Ahn, O. De Weck, Y. Geng, and D. Klabjan, “Column generation based heuristics for a generalized location routing problem with profits arising in space exploration,” Eur. J. Oper. Res., vol. 223, no. 1, pp. 47–59, 2012, doi: 10.1016/j.ejor.2012.06.018.

S. Abdinnour-Helm, “A hybrid heuristic for the uncapacitated hub location problem,” Eur. J. Oper. Res., vol. 106, no. 2–3, pp. 489–499, 1998, doi: 10.1016/S0377-2217(97)00286-5.

R. Abyazi-Sani and R. Ghanbari, “An efficient tabu search for solving the uncapacitated single allocation hub location problem,” Comput. Ind. Eng., vol. 93, pp. 99–109, 2016, doi: 10.1016/j.cie.2015.12.028.

J. W. Chinneck, B. Kristjansson, and M. J. Saltzman, “Operations research and cyber-infrastructure,” Oper. Res. Comput. Sci. Interfaces Ser., vol. 47, pp. 309–330, 2009, doi: 10.1007/978-0-387-88843-9.

N. Asgari, M. Rajabi, M. Jamshidi, M. Khatami, and R. Z. Farahani, “A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study,” Ann. Oper. Res., vol. 250, no. 2, pp. 279–308, 2017, doi: 10.1007/s10479-016-2248-7.

E. Ardjmand, G. Weckman, N. Park, P. Taherkhani, and M. Singh, “Applying genetic algorithm to a new location and routing model of hazardous materials,” Int. J. Prod. Res., vol. 53, no. 3, pp. 916–928, 2015, doi: 10.1080/00207543.2014.942010.

T. Aykin and G. F. Brown, “Interacting new facilities and location-allocation problems,” Transp. Sci., vol. 26, no. 3, pp. 212–222, 1992, doi: 10.1287/trsc.26.3.212.

M. Albareda-Sambola, J. A. Díaz, and E. Fernández, “A compact model and tight bounds for a combined location-routing problem,” Comput. Oper. Res., vol. 32, no. 3, pp. 407–428, 2005, doi: 10.1016/S0305-0548(03)00245-4.