Том 80, № 11 (2019)

Programmable Manufacturing Advisor (PMA) is a device intended to automate decision-making in manufacturing environment. Programming and installing a PMA at any production system makes it smart: it becomes capable of self-diagnosing and providing the Operations Manager with an advice for achieving the desired productivity improvement. In this paper, theoretical/analytical foundations of PMA are outlined, its software/hardware implementations are commented upon, and demonstrations of PMA-based Smart Production Systems are provided using an automotive underbody assembly system and a hot-dip galvanization plant.

Nonautonomous discrete-time control systems with uncertain nonlinearities and bounded external disturbances are considered. Based on the method of matrix comparison systems and the technique of difference linear matrix inequalities, an approach to solve the problems of state estimation, finite time boundedness with respect to given sets, the suppression of initial deviations and uncertain disturbances using a linear state feedback controller is developed. A method to design a controller with variable coefficients that guarantees the transition from one given ellipsoid to another under any disturbances bounded by the L_∞ norm is proposed.

We consider a linear stochastic programming problem with a deterministic objective function and individual probabilistic constraints. Each probabilistic constraint is a lower bound on the probability function equal to the probability of the fulfillment of a certain linear inequality. We propose to first represent probabilistic constraints in the form of equivalent inequalities for the quantile functions. After that, each quantile function is approximated using the confidence method. The main analytic tool is based on polyhedral approximation of the p-kernel for the multidimensional probability distribution. For the case when probability functions are defined by linear inequalities, constraints on quantile functions are with arbitrary accuracy approximated by systems of deterministic linear inequalities. As a result, the original problem is approximated by a linear programming problem.

The problem of reaching consensus in multiagent second-order systems without a spanning outgoing tree in the dependency digraph is considered. A theorem stating that the asymptotic behavior of the system is uniquely determined by the eigenprojector of the Laplacian matrix of the dependency digraph is proved. The earlier results established by the author and by Ren and Atkins in their papers are further generalized. For the case in which the dependency digraph contains no spanning outgoing tree, a regularization method is proposed.

A procedure for classifying objects with a hierarchical structure of relations (semantics) of features that takes into account their modalities is proposed. The concepts of semantics of features and their modalities are explained prior to the description of this procedure. A three-level model with a semantic hierarchy of features (objects—meta-features—subfeatures of objects) is considered. Meta-features are interpreted as semantic generalizations of the related subfeatures of objects. An important stage of the proposed procedure is the aggregation of the lower level subfeatures, taking into account their semantic connection with the meta-features. Aggregation leads to a significant reduction in the dimension of the original classification problem, which is now solved in terms of values of the aggregation function. As an example, the Dermatology sample from the well-known UCI Machine Learning repository is considered. This example shows that despite a considerable imbalance of the Dermatology sample, the results yielded by the proposed procedure are quite comparable with the best results of some well-known algorithms obtained on this sample.

We present the results of a comparative statistical analysis of the time for solving the asymmetric traveling salesman problem (ATSP) with the branch-and-bound method (without precalculation of the tour) and with a hybrid method. The hybrid method consists of the Lin–Kernighan–Helsgaun approximate algorithm used to calculate the initial tour and the branch-and-bound method. We show that using an approximate solution found with the Lin–Kernighan–Helsgaun algorithm can significantly reduce the search time for the exact solution to the traveling salesman problem using the branch-and-bound method for problems from a certain class. We construct a prediction of the search time for the exact solution by the branchand- bound method and by the hybrid algorithm. A computational experiment has shown that the proportion of tasks solved faster by the hybrid algorithm than by the branch-and-bound method grows with increasing problem dimension.

A set of models of sequentially growing complexity for enterprise multicriteria decision-making (output planning, borrowing/granting funds, investments in efficiency improvement and production capacity increase) is considered. Simulation results are presented, and also the development and application of system optimization methods to the modeling of production and economic activities of enterprises are discussed.