TY - JOUR
T1 - Structural classification of network models
AU - Voropajev, V. I.
AU - Ljubkin, S. M.
AU - Titarenko, B. P.
AU - Golenko-Ginzburg, D.
N1 - Funding Information:
This research has been partially supported by the Paul Ivanier Center of Robotics and Production Management, Ben-Gurion University of the Negev.
PY - 2000/10/1
Y1 - 2000/10/1
N2 - A newly developed structural classification of network models is suggested. It is based on singling out three main groups of characteristics which define both the structure and the parameters of network models. Those groups are (1) network elements (nodes, arrows, terms' restriction, logical links, etc.), (2) elements' parameters (number, function, set of variants, random variable, etc.) and (3) degree of alternativity (alternative logical operations at the node's input and output). The structural classification arranges an order in the variety of all the types of network models by using a three-dimension matrix. Each model can be characterised by a set of cells in the three-dimensional 'house'. The classification enables not only a description of the network models, but also a forecast of new models. The latter may be discovered either by filling in 'empty places' in the matrix or by implementing new attributes in the groups' characteristics.
AB - A newly developed structural classification of network models is suggested. It is based on singling out three main groups of characteristics which define both the structure and the parameters of network models. Those groups are (1) network elements (nodes, arrows, terms' restriction, logical links, etc.), (2) elements' parameters (number, function, set of variants, random variable, etc.) and (3) degree of alternativity (alternative logical operations at the node's input and output). The structural classification arranges an order in the variety of all the types of network models by using a three-dimension matrix. Each model can be characterised by a set of cells in the three-dimensional 'house'. The classification enables not only a description of the network models, but also a forecast of new models. The latter may be discovered either by filling in 'empty places' in the matrix or by implementing new attributes in the groups' characteristics.
KW - Alternative activities
KW - Network models
KW - Structural classification
UR - http://www.scopus.com/inward/record.url?scp=0033739046&partnerID=8YFLogxK
U2 - 10.1016/S0263-7863(99)00032-0
DO - 10.1016/S0263-7863(99)00032-0
M3 - Article
AN - SCOPUS:0033739046
SN - 0263-7863
VL - 18
SP - 361
EP - 368
JO - International Journal of Project Management
JF - International Journal of Project Management
IS - 5
ER -