Elaboration of the equipment replacement terms taking into account wear and tear and obsolescence

Inna Lapkina, Mykola Malaksiano

Abstract


The optimum terms of replacement of equipment subject to wear and tear and obsolescence with more advanced equipment with similar performance are investigated. To this end, the methodology of multi-criteria evaluation of equipment performance indicators when switching to a new type of equipment is proposed. The values of EAC (Equivalent Annual Cost), calculated for several equipment replacement cycles and for an infinite number of cycles are investigated. Estimates of the dispersion degree of EAC values depending on the service life of old and new equipment under conditions when the dynamics of operating costs is subject to random fluctuations are obtained. For this, covariance functions of random processes that describe the dynamics of the operating costs of old and new equipment were used. On the basis of covariance functions, estimates of the functions of standard deviations of EAC values are obtained. Using the obtained estimate of the degree of dispersion of equipment performance indicators, the multi-criteria optimization problem was investigated. This approach is of great practical interest, because for many enterprises, not only the average expected level of equipment performance indicators, but also the dispersion degree of the values of these indicators is of great importance. As a result of the research, the technique for planning the equipment replacement terms was developed. The proposed methodology allows justifying the terms of replacement of old equipment with new equipment, taking into account both the average expected EAC values and their level of fluctuations. The studies have shown that due to choosing the equipment renewal terms, it is possible to significantly reduce the degree of dispersion of equipment performance indicators, slightly sacrificing the average expected value.


Keywords


equipment replacement; equipment performance indicators optimization; equivalent annual cost; multi-criteria evaluation

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References


Adkins, R., Paxson, D. (2017). Replacement decisions with multiple stochastic values and depreciation. European Journal of Operational Research, 257 (1), 174–184. doi: 10.1016/j.ejor.2016.07.006

Lapkina, I., Malaksiano, M. (2018). Estimation of fluctuations in the performance indicators of equipment that operates under conditions of unstable loading. Eastern-European Journal of Enterprise Technologies, 1 (3 (91)), 22–29. doi: 10.15587/1729-4061.2018.123367

Nahorny, V. V. (2017). Automated control cutting based prediction of individual life of the cutting tool. Radio Electronics, Computer Science, Control, 3, 183–191. doi: 10.15588/1607-3274-2017-3-20

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Stutzman, S., Weiland, B., Preckel, P., Wetzstein, M. (2017). Optimal replacement policies for an uncertain rejuvenated asset. International Journal of Production Economics, 185, 21–33. doi: 10.1016/j.ijpe.2016.12.018

Malaksiano, N. A. (2012). On the stability of economic indicators of complex port equipment usage. Actual Problems of Economics, 12, 226–233.

Mihaylov, V. V. (1973). Nadezhnost' elektrosnabzheniya promyshlennyh predpriyatiy. Moscow: Energiya, 168.

Zambujal-Oliveira, J., Duque, J. (2011). Operational asset replacement strategy: A real options approach. European Journal of Operational Research, 210 (2), 318–325. doi: 10.1016/j.ejor.2010.09.011

Liu, B., Wu, J., Xie, M. (2015). Cost analysis for multi-component system with failure interaction under renewing free-replacement warranty. European Journal of Operational Research, 243 (3), 874–882. doi: 10.1016/j.ejor.2015.01.030

Lapkina, I. O., Malaksiano, M. O., Malaksiano, M. O. (2016). Optimization of the structure of sea port equipment fleet under unbalanced load. Actual Problems of Economics, 9, 364–371.

Lapkina, I. O., Malaksiano, M. O. (2016). Modelling and optimization of perishable cargo delivery system through Odesa port. Actual Problems of Economics, 3, 353–365.

Hagspiel, V., Huisman, K. J. M., Nunes, C. (2015). Optimal technology adoption when the arrival rate of new technologies changes. European Journal of Operational Research, 243 (3), 897–911. doi: 10.1016/j.ejor.2014.12.024

Adkins, R., Paxson, D. (2013). Deterministic models for premature and postponed replacement. Omega, 41 (6), 1008–1019. doi: 10.1016/j.omega.2013.01.002

Chronopoulos, M., Siddiqui, A. (2014). When is it Better to Wait for a New Version? Optimal Replacement of an Emerging Technology under Uncertainty. Norwegian School of Economics, Department of Business and Management Science. Available at: https://brage.bibsys.no/xmlui/bitstream/handle/11250/217638/1/DiscussionPaper.pdf

Nguyen, T. P. K., Yeung, T. G., Castanier, B. (2013). Optimal maintenance and replacement decisions under technological change with consideration of spare parts inventories. International Journal of Production Economics, 143 (2), 472–477. doi: 10.1016/j.ijpe.2012.12.003

Jones, T. W., Smith, D. (1982). An historical perspective of net present value and equivalent annual cost. Accounting Historians Journal, 9 (1), 103–110. doi: 10.2308/0148-4184.9.1.103

Anderson, B. D. O., Bitmead, R. R., Johnson, C. R., Kokotovic, Jr., P. Y., Kosut, R. L., Mareels, I. M. V., Praly, L., Riedle, B. D. (1986). Stability of adaptive systems: passivity and averaging analysis. MIT Press, 326.

Lutsenko, I. (2016). Definition of efficiency indicator and study of its main function as an optimization criterion. Eastern-European Journal of Enterprise Technologies, 6 (2 (84)), 24–32. doi: 10.15587/1729-4061.2016.85453

Jardine, A., Tsang, A. (2013). Maintenance, Replacement, and Reliability. Theory and Applications. CRC Press, 364. doi: 10.1201/b14937

Volkov, I. K., Zuyev, S. M., Tsvetkova, G. M. (1999). Sluchaynye protsessy. Moscow: Izd-vo MGTU, 448.

Podinovskiy, V. V., Nogin, V. D. (1982). Pareto-optimal'nye resheniya mnogokriterial'nyh zadach. Moscow: Nauka, 256.


GOST Style Citations


Adkins R., Paxson D. Replacement decisions with multiple stochastic values and depreciation // European Journal of Operational Research. 2017. Vol. 257, Issue 1. P. 174–184. doi: 10.1016/j.ejor.2016.07.006 

Lapkina I., Malaksiano M. Estimation of fluctuations in the performance indicators of equipment that operates under conditions of unstable loading // Eastern-European Journal of Enterprise Technologies. 2018. Vol. 1, Issue 3 (91). P. 22–29. doi: 10.15587/1729-4061.2018.123367 

Nahorny V. V. Automated control cutting based prediction of individual life of the cutting tool // Radio Electronics, Computer Science, Control. 2017. Issue 3. P. 183–191. doi: 10.15588/1607-3274-2017-3-20 

Zaloga V. A., Nagorniy V. M., Nagorniy V. V. Kontrol' dinamicheskogo sostoyaniya metalloobrabatyvayushchey tekhnologicheskoy sistemy i prognozirovanie ee resursa. Sumy: Sumskiy gosudarstvenniy universitet, 2016. 242 p.

Optimal replacement policies for an uncertain rejuvenated asset / Stutzman S., Weiland B., Preckel P., Wetzstein M. // International Journal of Production Economics. 2017. Vol. 185. P. 21–23. doi: 10.1016/j.ijpe.2016.12.018 

Malaksiano N. A. On the stability of economic indicators of complex port equipment usage // Actual Problems of Economics. 2012. Issue 12. P. 226–233.

Mihaylov V. V. Nadezhnost' elektrosnabzheniya promyshlennyh predpriyatiy. Moscow: Energiya, 1973. 168 p.

Zambujal-Oliveira J., Duque J. Operational asset replacement strategy: A real options approach // European Journal of Operational Research. 2011. Vol. 210, Issue 2. P. 318–325. doi: 10.1016/j.ejor.2010.09.011 

Liu B., Wu J., Xie M. Cost analysis for multi-component system with failure interaction under renewing free-replacement warranty // European Journal of Operational Research. 2015. Vol. 243, Issue 3. P. 874–882. doi: 10.1016/j.ejor.2015.01.030 

Lapkina I. O., Malaksiano M. O., Malaksiano M. O. Optimization of the structure of sea port equipment fleet under unbalanced load // Actual Problems of Economics. 2016. Issue 9. P. 364–371.

Lapkina I. O., Malaksiano M. O. Modelling and optimization of perishable cargo delivery system through Odesa port // Actual Problems of Economics. 2016. Issue 3. P. 353–365.

Hagspiel V., Huisman K. J. M., Nunes C. Optimal technology adoption when the arrival rate of new technologies changes // European Journal of Operational Research. 2015. Vol. 243, Issue 3. P. 897–911. doi: 10.1016/j.ejor.2014.12.024 

Adkins R., Paxson D. Deterministic models for premature and postponed replacement // Omega. 2013. Vol. 41, Issue 6. P. 1008–1019. doi: 10.1016/j.omega.2013.01.002 

Chronopoulos M., Siddiqui A. When is it Better to Wait for a New Version? Optimal Replacement of an Emerging Technology under Uncertainty // Norwegian School of Economics, Department of Business and Management Science. 2014. URL: https://brage.bibsys.no/xmlui/bitstream/handle/11250/217638/1/DiscussionPaper.pdf

Nguyen T. P. K., Yeung T. G., Castanier B. Optimal maintenance and replacement decisions under technological change with consideration of spare parts inventories // International Journal of Production Economics. 2013. Vol. 143, Issue 2. P. 472–477. doi: 10.1016/j.ijpe.2012.12.003 

Jones T. W., Smith D. An historical perspective of net present value and equivalent annual cost // Accounting Historians Journal. 1982. Vol. 9, Issue 1. P. 103–110. doi: 10.2308/0148-4184.9.1.103 

Stability of adaptive systems: passivity and averaging analysis / Anderson B. D. O., Bitmead R. R., Johnson C. R., Kokotovic Jr., P. Y., Kosut R. L., Mareels I. M. V., Praly L., Riedle B. D. MIT Press, 1986. 326 p.

Lutsenko I. Definition of efficiency indicator and study of its main function as an optimization criterion // Eastern-European Journal of Enterprise Technologies. 2016. Vol. 6, Issue 2 (84). P. 24–32. doi: 10.15587/1729-4061.2016.85453 

Jardine A., Tsang A. Maintenance, Replacement, and Reliability. Theory and Applications. CRC Press, 2013. 364 p. doi: 10.1201/b14937 

Volkov I. K., Zuyev S. M., Tsvetkova G. M. Sluchaynye protsessy. Moscow: Izd-vo MGTU, 1999. 448 p.

Podinovskiy V. V., Nogin V. D. Pareto-optimal'nye resheniya mnogokriterial'nyh zadach. Moscow: Nauka, 1982. 256 p.



DOI: https://doi.org/10.15587/1729-4061.2018.133690

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