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Haifa-A is the first of two case studies relating to the POWADIMA research project. It comprises about 20% of the city's water-distribution network and serves a population of some 60,000 from two sources. The hydraulic simulation model of the network has 126 pipes, 112 nodes, 9 storage tanks, 1 operating valve and 17 pumps in 5 discrete pumping stations. The complex energy tariff structure changes with hours of the day and days of the year. For a dynamically rolling operational horizon of 24 h ahead, the real-time, near-optimal control strategy is calculated by a software package that combines a genetic algorithm (GA) optimizer with an artificial neural network (ANN) predictor, the latter having replaced a conventional hydraulic simulation model to achieve the computational efficiency required for real-time use. This paper describes the Haifa-A hydraulic network, the ANN predictor, the GA optimizer and the demand-forecasting model that were used. Thereafter, it presents and analyses the results obtained for a full (simulated) year of operation in which an energy cost saving of some 25% was achieved in comparison to the corresponding cost of current practice. Conclusions are drawn regarding the achievement of aims and future prospects.

A methodology, based on the concept of Affinely Adjustable Robust Optimization, for optimizing daily operation of pumping stations is proposed, which takes into account the fact that a water distribution system in reality is unavoidably affected by uncertainties. For operation control, the main source of uncertainty is the uncertainty in the demand. Traditional methods for optimizing dynamical systems under uncertainty (Multistage Stochastic Programming) results in computationally intractable models already for small water distribution networks. The most popular optimization method for these problems is Dynamic Programming; however, in practice applications of this approach are restricted to networks with 1-2 pumping stations and/or 1-2 storages, because of severe computational difficulties arising in when state dimension of the controlled dynamical system exceeds 1-2. The approach presented in this paper provides a computationally tractable alternative to the outlined traditional methods in the cases when the problem under consideration, in the absence of uncertainty, can be formulated as a Linear Programming problem.

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We derive complexity estimates for two classes of deterministic networks: the Boolean networks S(B n, m), which compute the Boolean vector-functions B n, m , and the classes of graphs G(V P m, l , E), with overlapping communities and high density. The latter objects are well suited for the synthesis of resilience networks. For the Boolean vector-functions, we propose a synthesis of networks on a NOT, AND, and OR logical basis and unreliable channels such that the computation of any Boolean vector-function is carried out with polynomial information cost. All vertexes of the graphs G(V P m, l , E) are labeled by the trinomial (m 2 ± l, m)-partitions from the set of partitions P m, l. It turns out that such labeling makes it possible to create networks of optimal algorithmic complexity with highly predictable parameters. Numerical simulations of simple graphs for trinomial (m 2 ± l, m)-partition families (m = 3, 4,. .. , 9) allow for the exact estimation of all commonly known topological parameters for the graphs. In addition, a new topological parameter-overlapping index-is proposed. The estimation of this index offers an explanation for the maximal density value for the clique graphs G(V P m, l , E).

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The paper examines of the disjoint subsets of the strategies for the partition games to elucidate their "relative strength", i.e. to define which strategies to gain the wins account in the games such as, for instance, Lotto game. It can be inferred that analysis of the disjoint subsets of the set (n, m)-partitions enable one to choice the strategies with high "winning ability". Our focus is on the computing simulation of the elimination tournaments to check these assumptions. Actors in such elimination tournaments are the partitions and the rule are identical to that for a Lotto game. Computing simulation's results demonstrated