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The development of optimization algorithms for combinatorial problems is a complicated process, both guided and validated by the computational experiments over the different scenarios. Since the number of experiments can be very large and each experiment can take substantial execution time, distributing the load over the cloud speeds up the whole process significantly. In this paper we present the...
Stochastic Resource Constrained Project Scheduling (SRCPS) is among the hardest combinatorial problems. Exact calculations of interesting measures, such as expected project duration and the probability of satisfying the deadline, using known probabilities are in #P even for relaxed instances of the problem where resource constraints are ignored. The most common approach is to use substantial simulation...
A novel derivative-free algorithm for solving quasi-linear systems is presented. It resembles “classical” optimization approach but greatly simplifies computation, resulting in fast execution and numerical stability. Though the global convergence cannot be guaranteed, it turns out that the presented algorithm finds a solution as successfully as other commonly accepted methods. The algorithm is clearly...
In this paper two different methods for non-technical losses (NTL) detection are analyzed and new approach is proposed, based on the noticed drawbacks. It is shown that NTL can be successfully detected by a neural network trained by “artificial”, i.e., generated samples. This approach eliminates the need for many hard-to-obtain real life samples and the network can easily be trained to detect some...
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