02349nas a2200193   4500008004100000245008200041210006900123260000900192300001200201490000700213520168700220653002101907653002801928653002701956653002901983100001302012700001302025856011702038       2013                        eng d00aHierarchical clustering analysis method based on the grid with obstacle space0 aHierarchical clustering analysis method based on the grid with o  c2013  a76 - 820 v113 aThe advantage of grid-based clustering method is its fast processing speed. The speed of clustering algorithm and the number of data objects is unrelated. To discover any size and shape of the clusterÿit is by the number of units on each dimension in the data space. In this method, the amount of data and computation time does not matter, calculations and data entry of the order does not matter, does not require the number of k-means algorithm to pre-specified cluster and so on. Clustering problem with obstacle constraints has very strong practical value in the spatial clustering analysis, and has become a research hotspot in recent years. Under the condition of existing obstacles constraints, the vast majority of the spatial clustering algorithm can't effectively solve the problem of irregular obstructions. Thus it has a greater impact on the accuracy of the algorithm clustering results, and reduces the efficiency of the algorithm. To solve this problem, an obstacle constraint space grid-based hierarchical clustering algorithm, which is GSHCO algorithm, is proposed. The algorithm inherits the advantages of gridbased clustering algorithm, by defining the concept of barriers to grid to deal effectively with the obstacles of arbitrary shape, to achieve the purpose of found clusters of arbitrary shape; At the same time, the algorithm uses a hierarchical strategy which can effectively reduce the complexity of the algorithm with obstacles clustering and the algorithm is improved operating efficiency. The experimental results show that the GSHCO algorithm can deal with obstacles constrained clustering, and with higher performance and better clustering quality.10aCluster ensemble10aGrid spatial clustering10aMulti-scale clustering10aSpatial cluster analysis1 aShan, D.1 aYang, Z.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879084446&partnerID=40&md5=8173c506959188548e2182b57f3f31c201279nas a2200193   4500008004100000245005000041210004900091260000900140300001400149490000700163520067100170653002400841653002200865653002500887653002900912100001300941700001400954856011700968       2013                        eng d00aInformation sharing in supply chain: A review0 aInformation sharing in supply chain A review  c2013  a125 - 1300 v113 aThe importance of information sharing in supply chain management is a matter of consensus between practitioners and researchers. Information sharing can increase supply chain efficiency by reducing inventories and smoothing production. Studies from different and recognised authors are reviewed, compiled and analyzed in three aspects. First, the types of shared information in supply chain are summarized. After, the value of different shared information and the factors which affect its value are presented. Moreover, the methodologies used in literature are detailed. The paper concludes with some future directions for theory construction and empirical research.10aInformation sharing10aLiterature review10aSupply chain network10aSupply chains management1 aYang, Z.1 aZhang, L.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879070693&partnerID=40&md5=492576360136fd7384f5e3b18e51a4d1