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A fuzzy clustering-based hybrid method for a multi-facility location problem

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Abstract

A fuzzy clustering-based hybrid method for a multi-facility location problem is presented in this study. It is assumed that capacity of each facility is unlimited. The method uses different approaches sequentially. Initially, customers are grouped by spherical and elliptical fuzzy cluster analysis methods in respect to their geographical locations. Different numbers of clusters are experimented. Then facilities are located at the proposed cluster centers. Finally each cluster is solved as a single facility location problem. The center of gravity method, which optimizes transportation costs is employed to fine tune the facility location. In order to compare logistical performance of the method, a real world data is gathered. Results of existing and proposed locations are reported.

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References

  • Ayoub N., Martins R., Wang K., Seki H., Noka Y. (2007) Two levels decision system for efficient planning and implementation of bioenergy production. Energy Conversion and Management 48: 709–723

    Article  Google Scholar 

  • Babuska, R., Van Der Veen, P. J., & Kaymak, U. (2002). Improved covariance estimation for Gustafson Kessel clustering. In Proceedings of 2002 IEEE International Conference on Fuzzy Systems(pp. 1081–1085). Honolulu, Hawaii.

  • Balasko, B., Abonyi, J., & Feil, B. (2005). Fuzzy clustering and data analysis toolbox. Available: http://www.fmt.vein.hu/softcomp/fclusttoolbox.

  • Ballou R. (1999) Business Logistics Management (4th ed.). Prentice-Hall Inc., Upper Saddle River, New Jersey

    Google Scholar 

  • Bezdek J.C. (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Google Scholar 

  • Bezdek, J. C., Dunn, J. C. (1975). Optimal fuzzy partitions: A heuristic for estimating the parameters in a mixture of normal distributions. IEEE Transactions on Computers 835–838

  • Chepoi V., Dimitrescu D. (1999) Fuzzy clustering with structural constraints. Fuzzy Sets and Systems 105: 91–97

    Article  Google Scholar 

  • Döring C., Lesot M., Kruse R. (2006) Data analysis with fuzzy clustering methods. Computational Statistics and Data Analysis 51: 192–214

    Article  Google Scholar 

  • Esnaf S. (1998) Incremental change analysis-based fuzzy objective modeling in medicine: Serum lithium concentration prediction. Journal of Faculty of Business, Istanbul University 27: 19–28

    Google Scholar 

  • Gustafson, D. E., & Kessel, W. C. (1979). Fuzzy clustering with fuzzy covariance matrix. In Proceedings of the IEEE CDC (pp. 761–766). San Diego.

  • Hu T., Sheu J. (2003) A fuzzy-based customer classification method for demand-responsive logistical distribution operations. Fuzzy Sets and Systems 139: 431–450

    Article  Google Scholar 

  • Kenesei, T., Balasko, B., & Abony, J. (2006). A MATLAB toolbox and its web based variant for fuzzy cluster analysis. In Proceedings of the 7th International Symposium on Hungarian Researchers on Computational Intelligence, November 24–25, Budapest, Hungary.

  • Levin Y., Ben-Israel A. (2004) A heuristic method for large-scale multi-facility location problems. Computers and Operations Research 31: 257–272

    Article  Google Scholar 

  • Lu J., Yuan X., Yahagi T. (2006) A method of face recognition based on fuzzy clustering and parallel neural networks. Signal Processing 86: 2026–2039

    Article  Google Scholar 

  • Revelle C.S., Eiselt H.A. (2005) Location analysis: A Synthesis and Survey. European Journal of Operational Research 165: 1–19

    Article  Google Scholar 

  • Ross T.J. (1995) Fuzzy logic with engineering applications. McGraw-Hill, Inc, New York

    Google Scholar 

  • Sheu J. (2002) A fuzzy clustering-based approach to automatic freeway incident detection and characterization. Fuzzy Sets and Systems 128: 377–388

    Article  Google Scholar 

  • Sheu J. (2006) A novel dynamic resource allocation model for demand-responsive city logistics distribution operations. Transportation Research Part E: Logistics and Transportation Review 42(6): 445–472

    Article  Google Scholar 

  • Sheu J. (2007) A hybrid fuzzy optimization approach to customer grouping-based logistics distribution to operations. Applied Mathematical Modeling 31: 1048–1066

    Article  Google Scholar 

  • Sheu J., Chen Y., Lan L.W. (2005) A novel model for quick response to disaster relief distribution. Proceedings of the Eastern Asia Society for Transportation Studies 5: 2454–2462

    Google Scholar 

  • Sule D.R. (2001) Logistics of facility location and allocation. Marcel Dekker Inc, New York

    Google Scholar 

  • Turksen, I. B., & Esnaf, S. (1997). Financial data forecasting using fuzzy objective modeling. In Proceedings IV. Congress of the International Society of Fuzzy Management and Economics (Vol. 2, pp. 1–16).

  • Turksen, I. B., & Esnaf, S. (1998). Interaction of the level of fuzziness and the number of fuzzy clusters, In Proceedings of Second International Symposium on soft computing for Industry in the Third Biannual World Automation Congress(WAC), Anchorage (pp. 1–8), Alaska.

  • Zalik, K. R. (2006). Fuzzy C-means Clustering and Facility Location Problems. In Proceedings of the 10th IASTED Conference Artificial Intelligence and Soft Computing, August 28–30, Palma de Mallorca, Spain.

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Correspondence to Şakir Esnaf.

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Esnaf, Ş., Küçükdeniz, T. A fuzzy clustering-based hybrid method for a multi-facility location problem. J Intell Manuf 20, 259–265 (2009). https://doi.org/10.1007/s10845-008-0233-y

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  • DOI: https://doi.org/10.1007/s10845-008-0233-y

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