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Tangible and intangible incentives of key executives of management bodies of regions and districts: Modeling and assessment

Vol. 26, Iss. 10, OCTOBER 2020

Received: 23 July 2020

Received in revised form: 6 August 2020

Accepted: 20 August 2020

Available online: 29 October 2020

Subject Heading: INVESTING

JEL Classification: C63, E17, O21, O36

Pages: 2170–2192


Yashin S.N. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation


Koshelev E.V. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation


Borisov S.A. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation


Subject. This article explores the relationship between financial and non-financial motivations of top executives of State administration agencies in the context of aligning the interests of board-level managers and the general public.
Objectives. The article aims to create a model for assessing the financial and non-financial incentives of top managers of the administration bodies of regions and districts to develop a reasonable reward and recognition scheme.
Methods. For the study, we used a multi-objective genetic algorithm.
Results. The article presents a developed model for assessing the financial and non-financial incentives of top managers of the administration bodies of regions and districts. As well, it presents certain results of an analysis of board-level managers' incentives use through applying the model.
Relevance. The results obtained can be useful to government agencies to develop a reasonable system of financial and non-financial incentives of the agencies' top leadership.

Keywords: motivation, senior management, multi-objective genetic algorithm


  1. Yashin S.N., Koshelev E.V., Kuptsov A.V., Podshibyakin D.V. Investitsionnoe planirovanie modernizatsii oborudovaniya proizvodstvennoi kompanii: monografiya [Investment planning of modernization of a manufacturing company equipment: a monograph]. Nizhny Novgorod, Pechatnaya Masterskaya RADONEZH Publ., 2015, 201 p. URL: Link
  2. Limitovskii M.A. [Reputation, qualification and motivation as value drivers]. Rossiiskii zhurnal menedzhmenta = Russian Management Journal, 2009, vol. 7, no. 2, pp. 51–68. URL: Link (In Russ.)
  3. Khosrow-Pour M. Contemporary Advancements in Information Technology Development in Dynamic Environments. U.S.A., IGI Global, 2014, 410 p.
  4. Kalyanmoy D. Multi-Objective Optimization Using Evolutionary Algorithms. New York, John Wiley & Sons, Inc., 2009, 544 p.
  5. Baeck T., Fogel D.B., Michalewicz Z. Evolutionary Computations 2: Advanced Algorithms and Operators. CRC Press, 2000, 308 p.
  6. Fogel D.B., Fogel L.J., Porto V.W. Evolving Neural Networks. Biological Cybernetics, 1990, vol. 63, pp. 487–493. URL: Link
  7. Fogel D.B. Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. New York, IEEE Press, 1995, 272 p.
  8. Coello Coello C.A., Lamont G.B., van Veldhuizen D.A. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer Science & Business Media, 2007, 800 p.
  9. Branke J., Kalyanmoy D., Miettinen K., Slowinski R. (Eds). Multiobjective Optimization: Interactive and Evolutionary Approaches. Springer Science & Business Media, 2008, 470 p.
  10. Messac A., Ismail-Yahaya A., Mattson C.A. The Normalized Normal Constraint Method for Generating the Pareto Frontier. Structural and Multidisciplinary Optimization, 2003, vol. 25, pp. 86–98. URL: Link
  11. Erfani T., Utyuzhnikov S.V. Directed Search Domain: A Method for Even Generation of the Pareto Frontier in Multiobjective Optimization. Engineering Optimization, 2011, vol. 43, iss. 5, pp. 467–484. URL: Link
  12. Sim K.-B., Kim J.-Y. Solution of Multiobjective Optimization Problems: Coevolutionary Algorithm Based on Evolutionary Game Theory. Artificial Life and Robotics, 2004, vol. 8, pp. 174–185. URL: Link
  13. Rafiei S.M.R., Amirahmadi A., Griva G. Chaos Rejection and Optimal Dynamic Response for Boost Converter Using SPEA Multi-objective Optimization Approach. 2009 35th Annual Conference of IEEE Industrial Electronics, Porto, 2009, pp. 3315-3322. URL: Link
  14. Bemporad A., Muñoz de la Peña D. Multiobjective Model Predictive Control. Automatica, 2009, vol. 45, iss. 12, pp. 2823–2830. URL: Link
  15. Sendín J.O.H., Alonso A.A., Banga J.R. Efficient and Robust Multi-objective Optimization of Food Processing: A Novel Approach with Application to Thermal Sterilization. Journal of Food Engineering, 2010, vol. 98, iss. 3, pp. 317–324. URL: Link
  16. Motta R. de S., Afonso S.M.B., Lyra P.R.M. A Modified NBI and NC Method for the Solution of N-Multiobjective Optimization Problems. Structural and Multidisciplinary Optimization, 2012, vol. 46, pp. 239–259. URL: Link
  17. Domingo-Perez F., Lazaro-Galilea J.L., Wieser A. et al. Sensor Placement Determination for Range-Difference Positioning Using Evolutionary Multi-objective Optimization. Expert Systems with Applications, 2016, vol. 47, pp. 95–105. URL: Link
  18. Nguyen H.A., van Iperen Z., Raghunath S. et al. Multi-objective Optimisation in Scientific Workflow. Procedia Computer Science, 2017, vol. 108, pp. 1443–1452. URL: Link
  19. Abakarov A., Sushkov Yu., Mascheroni R.H. Multi-criteria Optimization and Decision-Making Approach for Improving of Food Engineering Processes. International Journal of Food Studies, 2012, vol. 2, no. 1, pp. 1–21. URL: Link
  20. Conn A.R., Gould N.I.M., Toint Ph.L. A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds. SIAM Journal on Numerical Analysis, 1991, vol. 28, no. 2, pp. 545–572. URL: Link
  21. Conn A.R., Gould N.I.M., Toint Ph.L. A Globally Convergent Lagrangian Barrier Algorithm for Optimization with General Inequality Constraints and Simple Bounds. Mathematics of Computation, 1997, vol. 66, no. 217, pp. 261–288. URL: Link
  22. Kolda T.G., Lewis R.M., Torczon V. A Generating Set Direct Search Augmented Lagrangian Algorithm for Optimization with a Combination of General and Linear Constraints. Technical Report SAND2006-5315. Oak Ridge, Sandia National Laboratories, August 2006, 44 p. URL: Link

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