DocumentCode :
265923
Title :
Clique structure and node-weighted centrality measures to predict distribution centre location in the supply chain management
Author :
Akanmu, Amidu A. G. ; Wang, Frank Z. ; Yamoah, Fred A.
Author_Institution :
Sch. of Comput., Univ. of Kent, Chatham, UK
fYear :
2014
fDate :
27-29 Aug. 2014
Firstpage :
100
Lastpage :
111
Abstract :
Much importance is attached to the weights on the edges in a network, but in actual fact what makes up a network is both the nodes and the edges linking up the network. It is therefore pertinent to investigate the effects and importance of the weights attributed unto the nodes in a network as well as the weights on the links of such networks as they both play important roles in determining the prominence or popularity of actors within any particular network. Principles of centrality measures were employed in the supply chain management to show that the weighted-ness of the edges/nodes together with the clique structure that emanates from it can be a pointer to centrality or otherwise of members of a group in the network of a distribution system. As expected, it was affirmed that the nodes belonging to the high clique members have a high percentage of being chosen/predicted as the most likely distribution centre. We examined the cliques of the weighted centrality matrix for the distributed system of a supply chain management network, and from the outcome we are able to predict a location of a new distribution centre in and around a particular area/region with an accuracy of more than 66%. In addition, the distinction between the notion of link-weightedness and node-weightedness were clarified.
Keywords :
graph theory; supply chain management; clique structure; distribution centre location; distribution system; graph theory; link-weightedness notion; node-weighted centrality measures; node-weightedness notion; supply chain management network; weighted centrality matrix; Accuracy; Cities and towns; Educational institutions; Roads; Supply chain management; Tuning; Weight measurement; Centrality; Clique; Networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Science and Information Conference (SAI), 2014
Conference_Location :
London
Print_ISBN :
978-0-9893-1933-1
Type :
conf
DOI :
10.1109/SAI.2014.6918178
Filename :
6918178
Link To Document :
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