One of the main problems in alternative network planning boils down to determining the optimal variant to carry out the considered simulated program. In this paper we will formulate the optimal variants choice criteria for the case of homogenous alternative networks which have been described in our publications [1–3].
homogenous alternative stochastic network, full and joint variants, optimal decision-making variant, multi-variant optimization, optimality indicator
1. INTRODUCTION
While examining homogenous alternative networks the problem focuses on determining the full variant of a design program which is optimal from the viewpoint of a certain accepted criterion. The difference between stochastic and deterministic alternative models reveals itself in future utilization of the results of such “multi-variant” optimization. In deterministic alternative networks the optimal variant has to be executed regardless of any future conditions and circumstances; furthermore, it may be recommended to be adopted as a kind of master plan whilst controlling the process of a complicated system design. For stochastic networks, when each of the competing variants has a non-zero implementation probability, control problems become more complicated, since we are facing the additional indeterminacy as to the ways of reaching the ultimate program's targets. Taking into account information regarding the stochastic variants quality, which has been acquired by means of the optimality criterion, the design decision-maker should direct his efforts to carrying out measures which ensure the most beneficial conditions of executing the determined optimal variant and those ones being close to it.
1. Golenko (Ginzburg) D.I., Livshitz S.E., Kesler S.Sh. Statistical Modeling in R&D Projecting. Leningrad, Leningrad University Press, 1976. (in Russian).
2. Golenko-Ginzburg D. Stochastic Network Models in R&D Projecting. Voronezh, Nauchnaya Kniga, 2010. (in Russian).
3. Golenko-Ginzburg D., Burkov V., Ben-Yair A. Planning and Controlling Multilevel Man-Machine Organization Systems under Random Disturbances / Ariel University Center of Samaria. Ariel, Elinir Digital Print, 2011.
