Abstract: In this paper, a generalized formulation of a classical single machine scheduling problem is considered. A set of n jobs characterized by their release dates, deadlines and a start time-dependent processing time function p(t) has to be processed on a single machine. The objective is to find a Pareto-optimal set of schedules with respect to the criteria max and makespan, where max is a non-decreasing function depending on the completion times of the jobs. We present an approach that allows to find an optimal schedule with respect to different scheduling criteria, such as the minimization of makespan, lateness or weighted lateness, tardiness and weighted tardiness etc. in time polynomially depending on the number of jobs. The complexity of the presented algorithm is O(n^3 max{log n , H, P}), where H and P are the complexity of calculating \varphi_j(t) and p(t), respectively.

The paper offers the research techniques for stable operation of discrete power supply systems in industrial enterprises. The methods for developing predictive models in control systems and decision-making support for nonlinear non-stationary objects are proposed. The methods are based on the application of associative search procedure to virtual model identification as well as Gramian techniques. The methods use intelligent process knowledge analysis. The knowledgebase is created and extended in realtime process operation. Intelligent algorithms are offered for predicting power plant dynamics in optimization tasks. The operator’s decision-making process is modeled using associative search algorithms. Gramian technique of stability analysis of discrete system is used for investigating linear virtual model stability.

The aim of the paper is to present a general approach to the statistical linearization based on information-theoretic measures of dependence. In the paper, a statistical linearization problem statement using the information-theoretic criterion under rather general conditions is proposed. It is based on a parameterized description of the system model under study combined with a corresponding method of estimation of the Shannon mutual information of the output variables of the initial system and its linearized model. Such a problem statement leads finally to a problem of the finite dimensional optimization. As a result, a constructive procedure of estimating the coefficients of the weight function of the linearized model is derived.

A minmax problem statement of a problem of the parameter identification of econometric models is considered, based on the entropy (in the Shannon sense) of the identification error. An interconnection of the minmax criterion proposed and a criterion of the maximum of the mutual information (in the sense of the Kullback-Leibler divergence) of the output data generating process and econometric model output process is shown. Simulation results confirming the validity of the entropy-based approach proposed are presented.

The paper offers the techniques for developing a driver’s decision-making support system embedded in vehicle’s onboard computer. The system is intended for maintaining vehicle’s normal operating parameters. During the motion the system models driver’s intellectual activity on the on-line assessment of vehicle’s operating conditions and the development of control actions. The system is predictive-diagnostic one because it prevents possible malfunctions but does not detect them ex post as against other available onboard control systems.