ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 6. Vol. 24. 2018

DOI: 10.17587/it.24.421-426

A. G. Shmeleva, PhD, Associate Professor, e-mail: shmeleva_a@mirea.ru, A. I. Ladynin, Postgraduate Student, Lecturer, e-mail: andrey.ladynin@hotmail.ru, A. V. Bakhmetiev, Student, e-mail: a_bahmetev@mail.ru, Moscow Technological University

Weighted Decisions Development for Complex Production Systems Management Using the Theory of Mass Service

The article presents intellectual system (DSS "ShAG") which is intended to support decision-making in production management tasks; it includes up-to-date methods for information processing and analysis. The Decision Support Systems development process implies taking into account application areas and user focus. The presented model visually demonstrates necessity for a comprehensive approach regarding input data, patterns, methods, algorithms for choosing the best production management strategy studies. The following article presents developed program realization, which is consistent with commonly used corporate and resource planning standards — Manufacturing Resource Planning (MRP) and Enterprise Resource Planning (ERP). These methods and models, presented in the following DSS, could be successfully used in planning tasks for small businesses and corporations. DSS modules should combine relevant processing and analyzing information methods, allowing the decision-maker receive a weighted conclusion supported by the results of the alternatives' analysis. One of the modules of the presented intellectual system is the models of queuing theory software implementation, which allows the throughput and fault tolerance of industrial systems assessment. The module is aimed at speeding up, cost lowering, simplifying the solution of transport logistics tasks, calculating the number of personnel (operators, dispatchers), designing the workshop throughput and other queuing theory problems that arise in high-tech enterprises. Developed software module usage example is presented for the production process parameters estimating problem. Usage of the proposed software will improve the engineering and production processes analysis.
Keywords: decision support system, automation, stream production, simulation modeling, queuing system, business planning, software implementation, intellectual system, information analysis, enterprise resource planning, algorithms, throughput analysis

P. 421–426

To the contents