doi: 10.17586/2226-1494-2019-19-2-292-298


RATIONAL SELECTION OF WORK PERFORMERS FOR INFORMATION SYSTEM DESIGN PROJECTS

A. A. Zaytsev, E. A. Kurennykh, V. A. Sudakov, O. T. Romanov


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Zaytsev A.A, Kurennykh A.E., Sudakov V.A., Romanov O.T. Rational selection of work performers for information system design projects. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 2,  pp. 292–298 (in Russian). doi: 10.17586/2226-1494-2019-19-2-292-298



Abstract

The paper deals with the process of rational selection of a work performer for development and implementation tasks on IT projects with corporate information systems of military-industrial and civil enterprises. The subject of this work is the development of methods, algorithms and software for collecting and analyzing statistical data about available work performers for certain tasks in behalf of the further decision support for a project manager. For the purpose of analyzing and processing data about developers and other employees we use information from RedMine project management system, as one of the most popular and common tool. Based on the information collected from the RedMine database for each available performer, a vector criterion is constructed. It becomes the basis for the project manager to estimate one’s suitability for the next task based on his own preference function, some versions of which are considered in this work. The scientific significance of this study lies in the fact that this approach can be applied in a multi-agent model for the process of development and implementation of information systems, which serves for optimal planning and control in the project management activities. Practical relevance consists in automatic performance of the person responsible for the distribution of tasks among the performers via formalized mathematical approach that provides a rational loading plan for available employees that leads to economic benefit.


Keywords: decision support, preference function, multi-agent modeling, data science, information system

Acknowledgements. This research is supported by the RFBR, project 18-00-00011 CIFR.

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