Heuristic procedure neural networks for the CMST problem
Computers and Operations Research - Neural networks in business
Fuzzy Mathematical Models in Engineering and Management Science
Fuzzy Mathematical Models in Engineering and Management Science
Ranking the sequences of fuzzy values
Information Sciences—Informatics and Computer Science: An International Journal
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Savings based ant colony optimization for the capacitated minimum spanning tree problem
Computers and Operations Research
Enhanced second order algorithm applied to the capacitated minimum spanning tree problem
Computers and Operations Research
Ranking of fuzzy numbers by sign distance
Information Sciences: an International Journal
Operations Research Letters
Evolutionary design of oriented-tree networks using Cayley-type encodings
Information Sciences: an International Journal
The Journal of Supercomputing
Degree constrained minimum spanning tree problem: a learning automata approach
The Journal of Supercomputing
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The classical Capacitated Minimum Spanning Tree Problem (CMSTP) deals with finding a minimum-cost spanning tree so that the total demand of the vertices in each subtree does not exceed the capacity limitation. In most of the CMSTP models, the edge costs and the demands of the vertices in the network are assumed to be known with certainty. This paper considers the CMSTP model, where the edge costs and/or the demands are only approximately known. A fast approximate reasoning algorithm, which is based on the Esau-Williams savings heuristic and fuzzy logic rules, is proposed. The computational results of the study based on the proposed approach are also reported.