Research into Merchant Logistics Center Scale Determining By Fuzzy Clustering

Ming-bao, Pang; Xie, Ling

doi:10.1109/icmit.2006.262348

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The distance is smaller than that of the clients in different logistics centers [12][13][14][15]. With FCM clustering method, we can get the uncertain degree of the clients belonging to the different classes or logistics centers [6] [11]. The problem for clients selecting the logistics centers and the amount is equal to that of determining the uncertain degree or the membership.…”

Section: Game Analysismentioning

confidence: 99%

“…The clients can exchange in different logistics centers. It is an uncertain problem and it is necessary to partition the different sorts according to the discrepancy of the clients selection by fuzzy clustering in order to analyze exactly the fuzzy effect [6] [11].…”

Section: Game Analysismentioning

confidence: 99%

“…The problem for clients selecting the logistics centers and the amount is equal to that of determining the uncertain degree or the membership. The basic clients or samples can be cities, parts of cities, section, countries, towns, villages, families or large companies according to the concrete situation [6].…”

Section: Game Analysismentioning

confidence: 99%

“…Traditionally the number and the scale of the logistics centers were determined by establishing the mixed nonlinear programming models [3][4]. In order to solve the reaction problem of the clients, bi-level programming was established by many researchers [2] [5][6]. There are three aspects problems in the method despite that it is scientific [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Regional Logistics System Bi-level Programming with Elastic Demand by Using Fuzzy C-means Clustering

Ming-bao

Dong

Zhao

2010

2010 Third International Symposium on Intelligent Information Technology and Security Informatics

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The bi-level programming of regional logistics system in elastic demand was studied by using fuzzy c-means clustering. Game relations among the government, the owners of the logistics centers, and the clients were discussed by system analysis method. The synthetic evaluation indices of 17 characteristic vectors were established. The problem is equal to that of determining the membership values to the given logistics centers or the clustering centers by using the thought of fuzzy c-means clustering. The clustering centers are some of the feasible sites that possess candidacy of the logistics centers. Stackelberg model was established. The upper level model is the problem for the government to seek maximal social benefits with elastic demand. The lower level model is the partition problem of clients' assignment. Genetic algorithm was adopted in the concrete course for solution. The simulation result shows its correctness.

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