Optimal Number and Sites of Regional Logistics Centers by Genetic Algorithm and Fuzzy C-mean Clustering

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

“…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.…”

“…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].…”

“…There are the government, the owners of the logistics centers, and the clients mainly in the regional logistics system [2][9] [11].…”

“…The number, the scale, the concrete site and the fuzzy character should be considered synchronously by uncertain theory [11]. Based on this, game relations among the government, the owners of the logistics centers and the clients were analyzed.…”

“…Programming of the regional logistics system should be studied by complex adaptive theory [8][9][10][11]. The number, the scale, the concrete site and the fuzzy character should be considered synchronously by uncertain theory [11].…”