THE PRINCIPLES OF DEVELOPING A MANAGEMENT DECISION SUPPORT SYSTEM FOR SCIENTIFIC EMPLOYEES

Zarifa Jabrayilova

Abstract


Employees engaged in mental work have become the most valuable assets of any organization in the 21st century. The satisfaction of those involved in mental work requires the provision of objectivity and transparency in their decision-making. This, in turn, entails the development of scientifically motivated decision making mechanisms and scientific-methodological approaches to evaluate their performance based on innovative technologies.

The main goal of this article is in development of the scientific and methodological framework for the establishment of a decision support system to manage the employees engaged in mental work and operating in uncertainty. In this regard, initially, the question of evaluating the activities of scientific workers is examined, its characteristic features are determined, and the fuzzy relation model is proposed as a multi-criterion issue formed in uncertainty. Taking into consideration the hierarchical structure of the criteria that allows evaluating the activities of scientific workers, a phased solution method based on an additive aggregation method is proposed. In accordance with the methodology, a functional scheme of the decision support system to manage the scientific personnel is developed. The working principle of each block and the interaction of the blocks are described. The rules for the employees’ management decisions are shown by referring to the knowledge production model.

Based on the proposed methodological approach, the implementation phases of the decision support system for the management of the scientific workers of the Institute of Information Technology of ANAS are described. To evaluate the employees’ performance, the tools to collect initial information, evaluate the system of criteria, define their importance coefficients and mathematical descriptions are provided. Some results of the system software are presented. The opportunities of the system based on the proposed methodology to support enterprise mangers to make scientifically justified decisions are provided.


Keywords


researcher; activity assessment; uncertainty; fuzzy relation model; additive aggregation; staff evaluation

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References


Mathis, R. L., Jackon, J. H., Valentine, S. R. (2014). Human Resource Management. Cengage Learning, 696.

Mammadova, M. H., Jabrayilova, Z. Q., Mammadzada, F. R. (2016). Fuzzy Multi-scenario Approach to Decision-Making Support in Human Resource Management. Studies in Fuzziness and Soft Computing, 342, 19–36. doi: http://doi.org/10.1007/978-3-319-32229-2_3

Mammadova, M. H., Jabrayilova, Z. G. (2018). Fuzzy multi-criteria method to support group decision making in human resource management. Studies in Fuzziness and Soft Computing, 361, 209–222. doi: http://doi.org/10.1007/978-3-319-75408-6_17

Drucker, P. F. (1999). Knowledge-Worker Productivity: The Biggest Challenge. California Management Review, 41 (2), 79–94. doi:http://doi.org/10.2307/41165987

Taylor, F. U. (1991). The Principles of scientific management. Moscow. Available at: http://gtmarket.ru/laboratory/basis/3631

Law of the Republic of Azerbaijan on Science (09.08.2016). Available at: https://president.az/articles/20785

Zlotnitsky, V. E. (2008). Factors of effective management of human resources of organization. Moscow, 190. Available at:www.dissercat.com/content/faktory-effektivnogo-upravleniya-chelovecheskimi-resursami-organizatsii

Mammadova, M., Jabrayilova, Z. (2014). Application of Fuzzy Optimization Method in Decision-Making for Personnel Selection. Intelligent Control and Automation, 5 (4), 190–204. doi: http://doi.org/10.4236/ica.2014.54021

Odegov, Yu. G., Abdurakhmanov, K. Kh., Kotova, L. R. (2011). Evaluation of the effectiveness of personnel work: a methodological approach. Moscow: Publishing House AlfaPress, 752.

Zaynetdinova, I. F. (2016). Evaluation of the employees’ activities of the organization. Yekaterinburg: University Pub. House, 120.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8 (3), 338–353. doi: http://doi.org/10.1016/s0019-9958(65)90241-x

Zadeh, L. A. (1976). The concept of a linguistic variable and its application to approximate decision-making. Moscow: Mir, 168.

Larichev, O. I. (2002). Theory and methods of decision-making, including the Chronicle of events in the Magic countries. Moscow: Logos, 392.

Miconi, S. V. (2009). Multi-criteria selection based on a finite set of alternatives. Saint Petersburg: Publishing House Lan, 272.

Mammadova, M. H. (1997). Decision-making based on knowledge bases with a fuzzy relational structure. Baku: Elm, 296.

Bellman, R. E., Zadeh, L. A. (1970). Decision-Making in a Fuzzy Environment. Management Science, 17 (4), 141–164. doi:http://doi.org/10.1287/mnsc.17.4.b141

Mammadova, M. H., Jabrayilova, Z. G. (2018). Decision-Making Support in Human Resource Management Based on Multi-Objective Optimization. TWMS Journal of Pure and Applied Mathematics, 9 (1), 52–72.

Mammadova, M. H., Djabrailova, Z. G., Nobari, S. M. (2010). Use of information about the importance of the criteria in the solution of personnel management problems. Problems of Cybermetics and Informatics. Baku, 83–86.

Neumann, J. V., Morgenstern, O. (2007). Theory of games and economic behavior. One of Princeton University presses, Notable Centenary Titles, 776.

Mammadovа, M., Jabrayilova, Z. (2018). Methodological approach to the human resource management in virtual organizations. EUREKA: Physics and Engineering, 3, 3–11. doi: http://doi.org/10.21303/2461-4262.2018.00642

Human resource core standards and checklist. HR Collaborative, 29. Available at: http://www.hrndgov.org/image/cache/hr-checklist.pdf

Public Sector Standards in Human Resource Management. Effective on and from 21 February 2011. Government of western Australia. Available at:https://publicsector.wa.gov.au/sites/default/files/documents/hrm_standards_3.pdf




DOI: http://dx.doi.org/10.21303/2461-4262.2019.00951

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