Mukaddas Arshidinova

Work place: Laboratory of Computational Methods and Software, Institute of Information and Computational Technologies, 050010, 125 Pushkin Str., Almaty, Republic of Kazakhstan

E-mail: mukaddas.arshidinova@proton.me

Website:

Research Interests: Software Deployment, Software Design

Biography

Mukaddas Arshidinova is a Lecturer of special disciplines at the Laboratory of Computational Methods and Software, Institute of Information and Computational Technologies. Her areas of academic interest include software design and development technology, algorithmization and programming, software development.

Author Articles
Algorithms for Solving Problems of Resources Allocation in the Management of Business Processes in Educational Organizations

By Azat Tashev Zhanar Takenova Mukaddas Arshidinova

DOI: https://doi.org/10.5815/ijmecs.2023.05.02, Pub. Date: 8 Oct. 2023

This article deals with the problem of optimal resources allocation in reference to the area that is currently relevant in the Republic of Kazakhstan, the educational system and management issues in educational organizations in rapidly changing economic and social conditions. A model for the optimal resources’ allocation in the management of business processes that exist in educational organizations has been developed using the example of one of the key business processes. Such research methods as search, survey, Fishbone diagrams and heuristic methods were used. A computational algorithm was developed and the testing results on the suggested example were presented. Comparative analysis shows that the developed computational algorithm based on the application of the linear programming method results in the optimal resources allocation in the considered business process. The analysis of existing methods reveals their limitations, particularly in dealing with dependent operations. The research findings and approaches have practical implications for improving the management system and enhancing the quality of business processes in educational organizations. The algorithms and models developed in this study can be applied not only to solve load distribution issues among teachers but also to address resource allocation problems in other areas of educational institutions.

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