L1 - Norm base Fuzzy C-means Clustering Algorithms for Rectilinear Min-sum Facility location Problem
Keywords:
fuzzy c-means clustering, optimize , rectilinear ,facility location problemAbstract
This paper describes a Fuzzy c-means clustering algorithms that seeks to simultaneously optimize the locations for rectilinear min-sum facility location problem. The reckoning measures establish that the acquire outcomes are not only theoretical interest, but also that the techniques developed may absolutely advance to considerably faster algorithm.
References
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2. Hakimi SL. Optimum distribution of switching centers in a communication network and some related graphtheoretic problems, Oper. Res. 1965; 13: 462-475.
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8. R.E. Kuenne and R.M. Soland, Exact and approximate solutions to the multisource Weber problem, Mathematical Programming. 1972; 3(1): 193-209.
9. Murtagh BA, Niwattisyawong SR. Efficient method for the mutidepot location and allocation problem, Journal of the Operational Research Society 1982; 33: 629-634.
10. Ernst AT, Krichnamoorthy M. Solution algorithms for the capacitated single allocation hub location problem, Annals of Operations Research. 1999; 86: 141-159.
11. Gong D, Gen M, Xu W et al. Yamazaku, Hybrid evolutionary method for obstacle location-allocation problem, International Journal of Computers and Industrial Engineering 1995; 29: 525-530.
12. Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms’, Plenum Press,New York. 1981.
13. Miyamoto S, Mukaidono M. Fuzzy c-means as a regularization and maximum entropy approach. Proc. of the 7th International Fuzzy Systems Association World Congress (IFSA’97). Prague, Czech. June 25-30, 1997; 2: 86-92.
14. Jajuga K. L1-Norm based fuzzy clustering, Fuzzy Sets and Systems. 1991; 39: 43-50.
15. Miyamoto S, Agusta Y. An Efficient Algorithm for l1 Fuzzy c-Means and Its Termination. Control and Cybernetics. 1995; 24(4): 421-436.
16. Kumar R, Gupta S. Application of Fuzzy c- means clustering for facility location and transportation problems.
17. Xinbo G, Xie W. Advances in theory and applications of fuzzy clustering. In Chinese Science Bulletin. 2000; 45: 961-970.
18. Zhaoqi B, Xiaobing X, Xiaoxu W et al. Pattern recognition.Published House of Tsinghua University, Beijing. 2011.
2. Hakimi SL. Optimum distribution of switching centers in a communication network and some related graphtheoretic problems, Oper. Res. 1965; 13: 462-475.
3. Cooper L. Location-allocation problems, Operational Research. 1963; 11: 331-344.
4. Megiddo N, Supowit KJ. On the complexity of some common geometric location problems. SIAM Journal on Computing 1984; 13: 182-196.
5. Badri MA. Combining the analytic hierarchy process and goal programming for global facility location-allocation problem. International Journal of Production Economic 1999; 62(1): 237-248.
6. Hodey M, Melachrinoudis E, Wu X. Dynamic expansion and location of an airport: A multiple objective approach, Transportation Research Part A - Policy and Practice 1997; 31: 403-417.
7. Zhou J, Liu B, Modeling capacitated location-allocation problem with fuzzy demands, Computers and Industrial Engineering, in press.
8. R.E. Kuenne and R.M. Soland, Exact and approximate solutions to the multisource Weber problem, Mathematical Programming. 1972; 3(1): 193-209.
9. Murtagh BA, Niwattisyawong SR. Efficient method for the mutidepot location and allocation problem, Journal of the Operational Research Society 1982; 33: 629-634.
10. Ernst AT, Krichnamoorthy M. Solution algorithms for the capacitated single allocation hub location problem, Annals of Operations Research. 1999; 86: 141-159.
11. Gong D, Gen M, Xu W et al. Yamazaku, Hybrid evolutionary method for obstacle location-allocation problem, International Journal of Computers and Industrial Engineering 1995; 29: 525-530.
12. Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms’, Plenum Press,New York. 1981.
13. Miyamoto S, Mukaidono M. Fuzzy c-means as a regularization and maximum entropy approach. Proc. of the 7th International Fuzzy Systems Association World Congress (IFSA’97). Prague, Czech. June 25-30, 1997; 2: 86-92.
14. Jajuga K. L1-Norm based fuzzy clustering, Fuzzy Sets and Systems. 1991; 39: 43-50.
15. Miyamoto S, Agusta Y. An Efficient Algorithm for l1 Fuzzy c-Means and Its Termination. Control and Cybernetics. 1995; 24(4): 421-436.
16. Kumar R, Gupta S. Application of Fuzzy c- means clustering for facility location and transportation problems.
17. Xinbo G, Xie W. Advances in theory and applications of fuzzy clustering. In Chinese Science Bulletin. 2000; 45: 961-970.
18. Zhaoqi B, Xiaobing X, Xiaoxu W et al. Pattern recognition.Published House of Tsinghua University, Beijing. 2011.
Published
2018-06-23
