Journal of Zhejiang University SCIENCE A

, Volume 13, Issue 10, pp 782–798 | Cite as

Location optimization of multiple distribution centers under fuzzy environment

  • Yong Wang
  • Xiao-lei Ma
  • Yin-hai Wang
  • Hai-jun Mao
  • Yong Zhang


Locating distribution centers optimally is a crucial and systematic task for decision-makers. Optimally located distribution centers can significantly improve the logistics system’s efficiency and reduce its operational costs. However, it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center. With growing logistics demands, multiple distribution centers become necessary to meet customers’ requirements, but few studies have tackled the multiple distribution center locations (MDCLs) problem. This paper presents a comprehensive algorithm to address the MDCLs problem. Fuzzy integration and clustering approach using the improved axiomatic fuzzy set (AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria. Then, technique for order preference by similarity to ideal solution (TOPSIS) is applied for evaluating and selecting the best candidate for each cluster. Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure. Results from a case study in Guiyang, China, reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection. This new method may easily be extended to address location planning of other types of facilities, including hospitals, fire stations and schools.

Key words

Multiple distribution centers Location selection Clustering algorithm Axiomatic fuzzy set (AFS) Technique for order preference by similarity to ideal solution (TOPSIS) 

CLC number



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  1. Awasthi, A., Chauhan, S.S., Goyal, S.K., 2011. A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modeling, 53(1):98–109. [doi:10.1016/j.mcm.2010.07.023]MathSciNetzbMATHCrossRefGoogle Scholar
  2. Buckley, J.J., 1985. Ranking alternatives using fuzzy numbers. Fuzzy Sets and Systems, 15(1):21–31. [doi:10.1016/ 0165-0114(85)90013-2]MathSciNetzbMATHCrossRefGoogle Scholar
  3. Chen, C.T., 2001. A fuzzy approach to select the location of distribution center. Fuzzy Sets and Systems, 118(1):65–73. [doi:10.1016/S0165-0114(98)00459-X]MathSciNetzbMATHCrossRefGoogle Scholar
  4. Chou, C.C., 2009. Integrated short-term and long-term MCDM model for solving location selection problem. Journal of Transportation Engineering, 135(11):880–893. [doi:10. 1061/(ASCE)TE.1943-5436.0000057]CrossRefGoogle Scholar
  5. Chou, S.Y., Chang, Y.H., Shen, C.Y., 2008. A fuzzy simple additive weighting system under group decision making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1):132–145. [doi:10.1016/j.ejor.2007.05.006]zbMATHCrossRefGoogle Scholar
  6. Chu, T.C., 2002. Facility location selection using fuzzy TOPSIS under group decisions. Fuzziness and Knowledge-Based Systems, 10(6):687–701. [doi:10.1142/S0218488502001739]MathSciNetzbMATHCrossRefGoogle Scholar
  7. Hakimi, S.L., Kuo, C.C., 1991. On a general network locationproduction-allocation problem. European Journal of Operational Research, 55(1):31–45. [doi:10.1016/0377-2217(91)90189-3]zbMATHCrossRefGoogle Scholar
  8. Hansen, P., Filho, E., Ribeiro, C.C., 1992. Location and sizing of offshore platforms for oil exploration. European Journal of Operational Research, 58(2):202–214. [doi:10.1016/0377-2217(92)90207-P]zbMATHCrossRefGoogle Scholar
  9. Heilpern, S., 1997. Representation and application of fuzzy numbers. Fuzzy Sets Systems, 91(2):259–268. [doi:10. 1016/S0165-0114(97)00146-2]MathSciNetzbMATHCrossRefGoogle Scholar
  10. José, L.P.T., Eugenio, P., Víctor, Y., 2012. Complete fuzzy scheduling and fuzzy earned value management in construction projects. Journal of Zhejiang University-SCIENCE A (Applied Physics and Engineering), 13(1): 56–68. [doi:10.1631/jzus.A1100160]Google Scholar
  11. Küçükaydin, H., Aras, N., Altınel, I.K., 2011. Competitive facility location problem with attractiveness adjustment of the follower: A bi-level programming model and its solution. European Journal of Operational Research, 208(3):206–220. [doi:10.1016/j.ejor.2010.08.009]MathSciNetzbMATHCrossRefGoogle Scholar
  12. Kahraman, C., Ruan, D., Dogan, I., 2003. Fuzzy group decision-making for facility location selection. Information Sciences, 157:135–153. [doi:10.1016/S0020-0255(03) 00183-X]zbMATHCrossRefGoogle Scholar
  13. Lee, C., 1993. The multiproduct warehouse location problem: applying a decomposition algorithm. International Journal of Physical Distribution & Logistics Management, 23(6):3–13. [doi:10.1108/09600039310044858]CrossRefGoogle Scholar
  14. Lee, D., Donnell, D., 2007. Analysis of nighttime drive behavior and pavement marking effects using fuzzy inference system. Journal of Computing in Civil Engineering, 21(3):200–210. [doi:10.1061/(ASCE)0887-3801(2007)21: 3(200)]CrossRefGoogle Scholar
  15. Li, R.J., 1999. Fuzzy method in group decision making. Computers & Mathematics with Applications, 38(1): 91–101. [doi:10.1016/S0898-1221(99)00172-8]MathSciNetzbMATHCrossRefGoogle Scholar
  16. Li, Y., Liu, X.D., Chen, Y., 2011. Selection of logistics center location using axiomatic fuzzy set and TOPSIS methodology in logistics management. Expert Systems with Applications, 38(6):7901–7908. [doi:10.1016/j.eswa.2010.12.161]CrossRefGoogle Scholar
  17. Liang, G.S., Wang, M.J.J., 1991. A fuzzy multi-criteria decision-making method for facility site selection. International Journal of Production Research, 29(11): 2313–2330. [doi:10.1080/00207549108948085]zbMATHCrossRefGoogle Scholar
  18. Lin, C.H., Ke, J.C., 2009. Optimal operating policy for a controllable queueing model with a fuzzy environment. Journal of Zhejiang University SCIENCE A, 10(2): 311–318. [doi:10.1631/jzus.A0820139]zbMATHCrossRefGoogle Scholar
  19. Liu, X.D., 1998a. The fuzzy theory based on AFS algebras and AFS structure. Journal of Mathematical Analysis and Applications, 217(2):459–478. [doi:10.1006/jmaa.1997.5718]MathSciNetzbMATHCrossRefGoogle Scholar
  20. Liu, X.D., 1998b. The fuzzy sets and systems based on AFS structure, EIalgebra and EII algebra. Fuzzy Sets and Systems, 95(2):179–188. [doi:10.1016/S0165-0114(96)00 298-9]MathSciNetzbMATHCrossRefGoogle Scholar
  21. Liu, X.D., Wang, W., Chai, T.Y., 2005. The fuzzy clustering analysis based on AFS theory. IEEE Transactions on Systems, Man and Cybernetics Part B, 35(5):1013–1027. [doi:10.1109/TSMCB.2005.847747]CrossRefGoogle Scholar
  22. Negi, D.S., 1984. Fuzzy Analysis and Optimization. PhD Thesis, Department of Industrial Engineering, Kansas State University, Kansas, USA.Google Scholar
  23. Shannon, C.E., 2001. A mathematical theory of communication. Mobile Computing and Communications Review, 5(1):3–55. [doi:10.1145/584091.584093]CrossRefGoogle Scholar
  24. Sun, H.J., Gao, Z.Y., Wu, J.J., 2008. A bi-level programming model and solution algorithm for the location of logistics distribution centers. Applied Mathematical Modelling, 32(4):610–616. [doi:10.1016/j.apm.2007.02.007]MathSciNetzbMATHCrossRefGoogle Scholar
  25. Syam, S.S., 2002. A model and methodologies for the location problem with logistical components. Computers and Operations Research, 29(9):1173–1193. [doi:10.1016/ S0305-0548(01)00023-5]MathSciNetzbMATHCrossRefGoogle Scholar
  26. Tyagi, R., Das, C., 1995. Manufacturer and warehouse selection for stable relationship in dynamic wholesaling and location problem. International Journal of Physical Distribution & Logistics Management, 25(6):54–72. [doi:10. 1108/09600039510093276]CrossRefGoogle Scholar
  27. Wang, Z.J., Qian, E.Y., 2007. A vague-set-based fuzzy multi-objective decision making model for bidding purchase. Journal of Zhejiang University SCIENCE A, 8(4): 644–650. [doi:10.1631/jzus.2007.A0644]MathSciNetzbMATHCrossRefGoogle Scholar
  28. Wey, W.M., Chang, Y.H., 2009. A comparative location study for the joint development station of a mass rapid transit system: A case in Taichung City. Environment and Planning B: Planning and Design, 36(4):573–587. [doi:10. 1068/b33135]CrossRefGoogle Scholar
  29. Yang, L.X., Ji, X.Y., Gao, Z.Y., Li, K.P., 2007. Logistics distribution centers location problem and algorithm under fuzzy environment. Journal of Computing and Applied Mathematics, 208(2):303–315. [doi:10.1016/]MathSciNetzbMATHCrossRefGoogle Scholar
  30. Yu, J., Liu, Y., Chang, G.L., Ma, W.J., Yang, X.G., 2011. Locating urban transit hubs: multi-criteria model and case study in China. Journal of Transportation Engineering, 137(12):944–952. [doi:10.1061/(ASCE)TE.1943-5436.0000275]CrossRefGoogle Scholar
  31. Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8(3):338–353. [doi:10.1016/S0019-9958(65)90241-X]MathSciNetzbMATHCrossRefGoogle Scholar
  32. Zhang, Y.J., Liang, D.Q., Tong, S.C., 2004a. On AFS algebrapart I. Information Sciences, 167(1):263–286. [doi:10. 1016/j.ins.2004.02.017]MathSciNetzbMATHCrossRefGoogle Scholar
  33. Zhang, Y.J., Liang, D.Q., Tong, S.C., 2004b. On AFS algebrapart II. Information Sciences, 167(1):287–303. [doi:10. 1016/j.ins.2003.10.007]MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yong Wang
    • 1
  • Xiao-lei Ma
    • 2
  • Yin-hai Wang
    • 2
  • Hai-jun Mao
    • 1
  • Yong Zhang
    • 1
  1. 1.School of TransportationSoutheast UniversityNanjingChina
  2. 2.Department of Civil and Environmental EngineeringUniversity of WashingtonSeattleUSA

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