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Of all publications in the section: 3
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Article
Krushinsky D., Goldengorin B. I. Computational Management Science. 2012. Vol. 9. No. 2. P. 323-338.

Despite the long history of the cell formation problem (CF) and availability of dozens of approaches, very few of them explicitly optimize the objective of cell formation. These scarce approaches usually lead to intractable formulations that can be solved only heuristically for practical instances. In contrast, we show that CF can be explicitly modelled via the minimum multicut problem and solved to optimality in practice (for moderately sized instances). We consider several real-world constraints that can be included into the proposed formulations and provide experimental results with real manufacturing data.

Added: Aug 13, 2012
Article
Vizgunov Arsenii, Goldengorin B., Kalyagin V. A. et al. Computational Management Science. 2014. Vol. 11. No. 1-2. P. 45-55.

We consider a market graph model of the Russian stock market. To study the peculiarity of the Russian market we construct the market graphs for different time periods from 2007 to 2011. As characteristics of constructed market graphs we use the distribution of correlations, size and structure of maximum cliques, and relationship between return and volume of stocks. Our main finding is that for the Russian market there is a strong connection between the volume of stocks and the structure of maximum cliques for all periods of observations. Namely, the most attractive Russian stocks have a strongest correlation between their returns. At the same time as far as we are aware this phenomenon is not related to the well developed USA stock market.

Added: Aug 20, 2013
Article
Kalyagin V. A., Koldanov A. P., Koldanov P.A. et al. Computational Management Science. 2013. Vol. 10. No. 2-3. P. 105-124.

A simple measure of similarity for the construction of the market graph is proposed. The measure is based on the probability of the coincidence of the signs of the stock returns. This measure is robust, has a simple interpretation, is easy to calculate and can be used as measure of similarity between any number of random variables. For the case of pairwise similarity the connection of this measure with the sign correlation of Fechner is noted. The properties of the proposed measure of pairwise similarity in comparison with the classic Pearson correlation are studied. The simple measure of pairwise similarity is applied (in parallel with the classic correlation) for the study of Russian and Swedish market graphs. The new measure of similarity for more than two random variables is introduced and applied to the additional deeper analysis of Russian and Swedish markets. Some interesting phenomena for the cliques and independent sets of the obtained market graphs are observed.

 

Added: Apr 3, 2013