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New local searches for solving the multi-source Weber problem

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Abstract

This paper presents three new heuristic approaches for the solution of the multi-source Weber problem in the plane: a constructive heuristic that finds a good starting solution, a decomposition approach which uses Delaunay triangulation, and a new efficient neighborhood structure based on the single facility limited distance median problem. A new heuristic incorporating all these approaches provided high quality solutions in reasonable computing time. We conclude that these heuristics successfully compete with the metaheuristic based methods found in the literature improving ten best known solutions. The ideas here may be extended to a variety of other continuous location as well as data mining problems.

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Notes

  1. The constructive algorithm idea was presented in Brimberg et al. (2012a) and appeared in the proceedings of that conference (Brimberg et al. 2012b).

  2. We thank Pawel Kalczynski, California State University at Fullerton, for solving the problems on Mathematica.

References

  • Aloise, D., & Hansen, P. (2009). Clustering. In D. Shier (Ed.), Handbook of discrete and combinatorial mathematics. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Berman, O., Drezner, Z., & Krass, D. (2011). Big segment small segment global optimization algorithm on networks. Networks, 58, 1–11.

    Article  Google Scholar 

  • Beyer, H. W. (1981). Standard mathematical tables. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Bongartz, I., Calamai, P. H., & Conn, A. R. (1994). A projection method for \(\ell _p\) norm location–allocation problems. Mathematical Programming, 66, 238–312.

    Article  Google Scholar 

  • Brimberg, J., Drezner, Z., Mladenovic, N., & Salhi, S. (2012a). Generating good starting solutions for the p-median problem in the plane. Presented at the EUROmC-XXVIII-VNS conference, October, 2012, Belgrade, Serbia.

  • Brimberg, J., Hansen, P., Mladenović, N., & Taillard, E. (2000). Improvements and comparison of heuristics for solving the uncapacitated multisource Weber problem. Operations Research, 48, 444–460.

    Article  Google Scholar 

  • Brimberg, J., Hansen, P., & Mladenović, N. (2006). Decomposition strategies for large-scale continuous location–allocation problems. IMA Journal of Management Mathematics, 17, 307–316.

    Article  Google Scholar 

  • Brimberg, J., Hansen, P., Mladenović, N., & Salhi, S. (2008). A survey of solution methods for the continuous location–allocation problem. International Journal of Operations Research, 5, 1–12.

    Google Scholar 

  • Brimberg, J., Drezner, Z., Mladenovic, N., & Salhi, S. (2012b). Generating good starting solutions for the p-median problem in the plane. Electronic Notes in Discrete Mathematics, 39, 225–232.

    Article  Google Scholar 

  • Brimberg, J., & Drezner, Z. (2013). A new heuristic for solving the p-median problem in the plane. Computers and Operations Research, 40, 427–437.

    Article  Google Scholar 

  • Brimberg, J., Drezner, Z., Mladenović, N., & Salhi, S. (2014). A new local search for continuous location problems. European Journal of Operational Research, 232, 256–265.

    Article  Google Scholar 

  • Carlsson, S. (1984). Improving worst-case behavior of heaps. BIT Numerical Mathematics, 24, 14–18.

    Article  Google Scholar 

  • Carrizosa, E., Mladenović, N., & Todosijevic, R. (2011). Sum-of-squares clustering on networks. Yugoslav Journal of Operations Research, 21, 157–161.

    Article  Google Scholar 

  • Chen, R. (1983). Solution of minisum and minimax location–allocation problems with euclidean distances. Naval Research Logistics Quarterly, 30, 449–459.

    Article  Google Scholar 

  • Chen, P. C., Hansen, P., Jaumard, B., & Tuy, H. (1998). A fast algorithm for the greedy interchange for large-scale clustering and median location problems by D.-C. programming. Operations Research, 46, 548–562.

    Article  Google Scholar 

  • Cooper, L. (1963). Location–allocation problems. Operations Research, 11, 331–343.

    Article  Google Scholar 

  • Cooper, L. (1964). Heuristic methods for location–allocation problems. SIAM Review, 6, 37–53.

    Article  Google Scholar 

  • Drezner, Z. (1984). The planar two-center and two-median problems. Transportation Science, 18, 351–361.

    Article  Google Scholar 

  • Drezner, Z., Mehrez, A., & Wesolowsky, G. O. (1991). The facility location problem with limited distances. Transportation Science, 25, 183–187.

    Article  Google Scholar 

  • Drezner, Z., & Suzuki, A. (2004). The big triangle small triangle method for the solution of non-convex facility location problems. Operations Research, 52, 128–135.

    Article  Google Scholar 

  • Drezner, Z. (2013). Solving planar location problems by global optimization. Logistics Research, 6, 17–23.

    Article  Google Scholar 

  • Eilon, S., Watson-Gandy, C. D. T., & Christofides, N. (1971). Distribution management. New York, NY: Hafner.

    Google Scholar 

  • Erlenkotter, D. (1978). A dual-based procedure for uncapacitated facility location. Operations Research, 26, 992–1009.

    Article  Google Scholar 

  • Feldman, E., Lehrer, F. A., & Ray, T. L. (1966). Warehouse location under continuous economies of scale. Management Science, 12, 670–684.

    Article  Google Scholar 

  • Gabow, H. N., Galil, Z., Spencer, T., & Tarjan, R. E. (1986). Efficient algorithms for finding minimum spanning trees in undirected and directed graphs. Combinatorica, 6, 109–122.

    Article  Google Scholar 

  • Hansen, P., Peeters, D., & Thisse, J.-F. (1981). On the location of an obnoxious facility. Sistemi Urbani, 3, 299–317.

    Google Scholar 

  • Hansen, P., & Mladenović, N. (1997). Variable neighborhood search for the \(p\)-median. Location Science, 5, 207–226.

    Article  Google Scholar 

  • Hansen, P., Mladenović, N., & Taillard, É. (1998). Heuristic solution of the multisource Weber problem as a p-median problem. Operations Research Letters, 22, 55–62.

    Article  Google Scholar 

  • Hansen, P., Brimberg, J., Urosević, D., & Mladenović, N. (2007). Primal-dual variable neighborhood for the simple plant location problem. INFORMS Journal of Computing, 19, 552–564.

    Article  Google Scholar 

  • Hilbert, D., & Cohn-Vossen, S. (1956). Geometry and the Imagination. Chelsea Publishing Company, New York. English translation of Anschauliche Geometrie (1932).

  • Krau, S. (1997). Extensions du problème de Weber. PhD thesis, École Polytechnique de Montréal.

  • Kuehn, A. A., & Hamburger, M. J. (1963). A heuristic program for locating warehouses. Management Science, 9, 643–666.

    Article  Google Scholar 

  • Kuenne, R. E., & Soland, R. M. (1972). Exact and approximate solutions to the multisource weber problem. Mathematical Programming, 3, 193–209.

    Article  Google Scholar 

  • Lee, D. T., & Schachter, B. J. (1980). Two algorithms for constructing a Delaunay triangulation. International Journal of Parallel Programming, 9(3), 219–242.

    Google Scholar 

  • Megiddo, N., & Supowit, K. J. (1984). On the complexity of some common geometric location problems. SIAM Journal on Computing, 13, 182–196.

    Article  Google Scholar 

  • Mehlhorn, K., & Sanders, P. (2008). Algorithms and data structures: The basic toolbox. Berlin: Springer.

    Google Scholar 

  • Moreno-Pérez, J. A., Rodrìguez, C., & Jimenez, N. (1991). Heuristic cluster algorithm for multiple facility location–allocation problem. RAIRO, 25, 97–107.

    Google Scholar 

  • Murtagh, B. A., & Niwattisyawong, S. R. (1982). An efficient method for the multi-depot location–allocation problem. Journal of the Operational Research Society, 33, 629–634.

    Google Scholar 

  • Ohya, T., Iri, M., & Murota, K. (1984). Improvements of the incremental method of the Voronoi diagram with computational comparison of various algorithms. Journal of the Operations Research Society of Japan, 27, 306–337.

    Google Scholar 

  • Ostresh Jr, L. M. (1973). TWAIN—exact solutions to the two-source location–allocation problem. In Rushton, G., Goodchild, M., & Ostresh Jr., L. (Eds.), Computer programs for location-allocation problems (pp. 15–28).

  • Ostresh, L. M, Jr. (1975). An efficient algorithm for solving the two center location–allocation problem. Journal of Regional Science, 15, 209–216.

    Article  Google Scholar 

  • Plastria, F. (1992). GBSSS, the generalized big square small square method for planar single facility location. European Journal of Operational Research, 62, 163–174.

    Article  Google Scholar 

  • Reinelt, G. (1991). TSLIB a traveling salesman library. ORSA Journal on Computing, 3, 376–384.

    Article  Google Scholar 

  • Rosing, K. E. (1992). An optimal method for solving the (generalized) multi-Weber problem. European Journal of Operational Research, 58, 414–426.

    Article  Google Scholar 

  • Rosing, K. E., & Harris, B. (1992). Algorithmic and technical improvements: Optimal solutions to the (generalized) multi-Weber problem. Papers in Regional Science, 71, 331–352.

    Article  Google Scholar 

  • Rote, G., & Woeginger, G. (1989). Geometric clusterings. Fachbereich Mathematik: Freie University.

    Google Scholar 

  • Salhi, S., & Atkinson, R. A. (1995). Subdrop: A modified drop heuristic for location problems. Location Science, 3, 267–273.

    Article  Google Scholar 

  • Schöbel, A., & Scholz, D. (2010). The big cube small cube solution method for multidimensional facility location problems. Computers and Operations Research, 37, 115–122.

    Article  Google Scholar 

  • Sugihara, K., & Iri, M. (1994). A robust topology-oriented incremental algorithm for Voronoi diagram. International Journal of Computational Geometry and Applications, 4, 179–228.

    Article  Google Scholar 

  • Teitz, M. B., & Bart, P. (1968). Heuristic methods for estimating the generalized vertex median of a weighted graph. Operations Research, 16, 955–961.

    Article  Google Scholar 

  • Tornqvist, G., Nordbeck, S., Rystedt, B., & Gould, P. (1971). Multiple location analysis. In Lund Studies in Geography, Ser C., General, Mathematical and Regional Geography, No. 12. University of Lund, Sweden.

  • Voss, S. (1996). A reverse elimination approach for the p-median problem. Studies in Locational Analysis, 8, 49–58.

    Google Scholar 

  • Wendell, R. E., & Hurter, A. P. (1973). Location theory, dominance and convexity. Operations Research, 21, 314–320.

    Article  Google Scholar 

  • Whitaker, R. (1983). A fast algorithm for the greedy interchange for large-scale clustering and median location problems. INFOR, 21, 95–108.

    Google Scholar 

Download references

Acknowledgments

We would like to thank the referees for their time and constructive comments that helped to improve the presentation as well as the content of the paper. This research had been supported in part by a Natural Sciences and Engineering Research Council of Canada Discovery Grant (NSERC #20541-2008). Work of Nenad Mladenovic was conducted at National Research University Higher School of Economics, Russia and supported by RSF Grant 14-41-00039.

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Drezner, Z., Brimberg, J., Mladenović, N. et al. New local searches for solving the multi-source Weber problem. Ann Oper Res 246, 181–203 (2016). https://doi.org/10.1007/s10479-015-1797-5

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