DOI: https://doi.org/10.15587/1729-4061.2015.35950

Identification of target system operations. determination of the value of the complex costs of the target operation

Igor Lutsenko

Abstract


Currently, the increase in financial returns from economic operations is constrained in view of the lack of a single efficiency criterion, which allows uniquely identify the business operation by their main feature - the possibility of obtaining the maximum value added (profit).

One of the main scientific steps on the way to obtaining the formula of efficiency is developing the "resource intensity" indicator. The development of this indicator was based on the model of the deployed operation and determination of the time of the actual completion of the target operation, which does not coincide with the traditional notion of the time of completion of economic operations.

For processes with distributed parameters, an expression for determining the resource intensity using numerical methods was derived.

For economic operations, which can be reduced to simple operations, an analytical expression of resource intensity was obtained.

Using mathematical modeling methods it was revealed that in the case of a fixed value of expert (cost) estimate of output products of the operation, the minimum resource intensity of the operation indicates a maximum efficiency operation with respect to the target product of the operation.

Development of the resource intensity of economic operations is the final step towards the development of cybernetic (interdisciplinary) efficiency indicator that allows to maximize the value added (profit) of economic operations.


Keywords


target operation; complex costs; resource intensity of target operation; costs of operation

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References


Lutsenko, I.(2014). Deployed model of extremal system operation for solving optimal management problems. Eastern-European Journal of Enterprise Technologies, 5/2 (71), 61–66. doi: 10.15587/1729-4061.2014.28592

Ehrlenspiel, K., Kiewert, A., Lindemann, U. (2007). Cost-Efficient Design. Springer, 544.

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Tsai, J.-T., Fang, J.-C., Chou, J.-H. (2013). Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Computers & Operations Research, 40 (12), 3045–3055. doi: 10.1016/j.cor.2013.06.012

Zheng, Y.-J.,Ling, H.-F., Xue, J.-Y. (2014). Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeogrephic barriers and differentiations. Computers & Operations Research, 50, 115–127. doi: 10.1016/j.cor.2014.04.013

Lutsenko, I.(2014) Identification of target system operations. 1. Determination of the time of the actual completion of the target operation. Eastern-European Journal of Enterprise Technologies, 6/2 (72), 42-47. doi: 10.15587/1729-4061.2014.28040

Lutsenko,I.A. (2014). Samples. Krivoy Rog. Available at: http://uk.effli.info/index.php/samples

Lutsenko, I. (2014). Systems engineering of optimal control I. Synthesis of the structure of the technological product conversion system (part 1). Eastern-European Journal of Enterprise Technologies, 6/2 (72), 28–37. doi: 10.15587/1729-4061.2014.28724


GOST Style Citations


1. Lutsenko, I. Deployed model of extremal system operation for solving optimal management problems [Text] / I. Lutsenko // Eastern-European Journal of Enterprise Technologies. – 2014. – Vol. 5, Issue 2 (71). – P. 61–66. doi: 10.15587/1729-4061.2014.28592

2. Ehrlenspiel, K. Cost-Efficient Design [Text] / K. Ehrlenspiel, A. Kiewert, U. Lindemann. – Springer, 2007. – 544 p.

3. Lapygin, Y. Cost Management in the enterprise. [Text] / Y. Lapygin, N. Prokhorov. – Eksmo, 2007. – 102 p.

4. Berk, J. Cost Reduction and Optimization for Manufacturing and Industrial Companies [Text] / J. Berk. – Wiley, 2010. – 258 p. doi: 10.1002/9780470643815 

5. Ghiani, G. Operations research in solid waste management: A survey of strategic and tactical issues [Text] / G. Ghiani, D. Laganà, E. Manni, R. Musmanno, D. Vigo // Computers & Operations Research. – 2014. – Vol. 44. – P. 22–32. doi: 10.1016/j.cor.2013.10.006 

6. Tsai, J.-T. Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm [Text] / J.-T. Tsai, J.-C. Fang, J.-H. Chou // Computers & Operations Research. – 2013. – Vol. 40, Issue 12. – P. 3045–3055. doi: 10.1016/j.cor.2013.06.012 

7. Zheng, Y.-J. Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeogrephic barriers and differentiations [Text] / Y.-J. Zheng, H.-F. Ling, J.-Y. Xue // Computers & Operations Research. – 2014. – Vol. 50. – P. 115–127. doi: 10.1016/j.cor.2014.04.013 

8. Lutsenko, I. Identification of target system operations. 1. Determination of the time of the actual completion of the target operation [Text] / I. Lutsenko // Eastern-European Journal of Enterprise Technologies. – 2014. – Vol. 6, Issue 2 (72). – P. 42–47. doi: 10.15587/1729-4061.2014.28040

9. Lutsenko, I. A. Samples [Electronic resource] / I. A. Lutsenko. – Krivoy Rog, 2014. – Available at: http://uk.effli.info/index.php/samples

10. Lutsenko, I. Systems engineering of optimal control I. Synthesis of the structure of the technological product conversion system  (part1) [Text] / I. Lutsenko // Eastern-European Journal of Enterprise Technologies. – 2014. – Vol. 6, Issue 2 (72). – P. 28–37. doi: 10.15587/1729-4061.2014.28724







Copyright (c) 2015 Игорь Анатольевич Луценко

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061