Construction and analysis of the model for stochastic optimization of inventory management at a ship repair yard




shipyard, queueing system, materials stocks, risk of idle vessels, optimal inventory management


A stochastic model of work of inventory management system at a ship repair yard (SRY) has been developed. In order to account for factors related to uncertainties and risks (random moments of arrival of ships at SRY, random volumes of repairs), it has been proposed to apply the apparatus of Markov drift processes for modeling. These processes make it possible to take into consideration the discrete character of change in the number of vessels at SRY, as well as the ongoing character of fluctuation in the inventory level of materials in warehouse. In this case, docks at SRY are interpreted as a queueing system. It is also assumed that the restocking of materials at a warehouse and their utilization during repair of ships is carried out continuously, at constant intensities, but depending on the availability of a material in warehouse. The result of this study is the stated problem on stochastic optimization of intensities in the resupply of materials based on the criterion of minimum cumulative average current expenses of the yard, which also take into consideration the losses associated with additional downtime of ships due to the lack of materials in warehouse during repair. It has been shown that the results obtained are important to the practical operation of SRY supply department as they make it possible to build a strategy for the replenishment of materials in stock at SRY under conditions of time-dependent non-uniformity in the need for ship repairs. From a theoretical point of view, the obtained results demonstrate a possibility of using the apparatus of Markov drift processes to solve various problems on optimal inventory control under conditions of random fluctuations in the demand for materials in warehouse.

Author Biographies

Igor Petrov, National University "Odessa Maritime Academy" Didrikhsona str., 8, Odessa, Ukraine, 65029

PhD, Professor

Department of Sea Transportation

Mykhaylo Postan, Odessa National Maritime University Mechnikov str., 34, Odessa, Ukraine, 65029

Doctor of Economic Sciences, Professor, Head of Department

Department of Management & Marketing


  1. Rynok sudoremonta: David protiv Goliafa. Porty Ukrainy. Available at:
  2. Optimisticheskaya tragediya sudostroeniya Ukrainy. Available at:
  3. Shahov, A. V., Bokareva, M. O. (2014). Upravlenie riskami v sudoremontnyh proektah. Visnyk NTU «KhPI». Seriya: Stratehichne upravlinnia, upravlinnia portfeliamy, prohramamy ta proektamy, 2 (1045), 87–95.
  4. Shahov, A. V., Chimshir, V. I. (2006). Proektno-orientirovannoe upravlenie funkcionirovaniem remontoprigodnyh tekhnicheskih sistem. Odessa: Feniks, 213.
  5. Aleksandrovskaya, N. I., Shahov, V. I., Shahov, A. V. (2011). Risko-orientirovannaya strategiya tekhnicheskogo obsluzhivaniya i remonta sudov. Metody ta zasoby upravlinnia rozvytkom transportnykh system, 17, 7–17.
  6. Kovalenko, I. I., Shved, A. V., Melnik, A. V. (2014). Probability analysis of risk-contributing factors in organizational tasks of ship repair. Shipbuilding and marine infrastructure, 2 (2), 111–119. doi:
  7. Charris, E. L. S., Arboleda, C. D. P. (2013). Simulation model of the supply chain on a naval shipyard. International Journal of Industrial and Systems Engineering, 13 (3), 280. doi:
  8. Pinha, D., Ahluwalia, R. (2014). Decision Support System for Production Planning in the Ship Repair Industry. Industrial and Systems Engineering Review, 2 (1), 52–61.
  9. He, L., Huang, X., Liu, X. (2013). Production Management Modelling of Ship Repair Process Based on MAS. Information Technology Journal, 12 (3), 498–501. doi:
  10. Gholami, A., Mirzazadeh, A. (2018). An inventory model with controllable lead time and ordering cost, log-normal-distributed demand, and gamma-distributed available capacity. Cogent Business & Management, 5 (1), 1–17. doi:
  11. Nasrabadi, M., Mirzazadeh, А. (2016). The Inventory System Management Under Uncertain Conditions and Time Value of Money. International Journal of Supply and Operations Management, 3 (1), 1192–1214.
  12. Brodeckiy, G. L. (2011). Ekonomiko-matematicheskie metody i modeli v logistike: potoki sobytiy i sistemy obsluzhivaniya. Moscow: Akademiya, 272.
  13. Gnedenko, B. V., Kovalenko, I. N. (2005). Vvedenie v teoriyu massovogo obsluzhivaniya. Moscow.: KomKniga, 400.
  14. Postan, M. (2008). Application of Markov Drift Processes to Logistical Systems Modeling. Dynamics in Logistics, 443–455. doi:
  15. Morozova, I., Postan, M., Shyryaeva, L. (2011). Optimization of Spare Parts Lot Size for Supply of Equipment’s Park. Dynamics in Logistics, 105–113. doi:
  16. Postan, M. Y. (2015). Application of Semi-Markov Drift Processes to Logistical Systems Modeling and Optimization. Lecture Notes in Logistics, 227–237. doi:
  17. Postan, M., Kushnir, L. (2016). A method of determination of port terminal capacity under irregular cargo delivery and pickup. Eastern-European Journal of Enterprise Technologies, 4 (3 (82)), 30–37. doi:
  18. Postan, M. Ya. (1992). Ob odnom klasse smeshannyh markovskih processov i ih primenenie v teorii teletrafika. Problemy peredachi informacii, 28 (3), 40–53.
  19. Сohen, J. W., Boxma, O. J. (2000). Boundary Value Problems in Queueing System Analysis. Elsevier, 404.
  20. Avramchuk, E. F., Vavilov, A. A., Emel'yanov, S. V. et. al.; Emel'yanov, S. V. et. al. (Eds.) (1988). Tekhnologiya sistemnogo modelirovaniya. Moscow: Mashinostroenie; Berlin: Tekhnik, 520.




How to Cite

Petrov, I., & Postan, M. (2018). Construction and analysis of the model for stochastic optimization of inventory management at a ship repair yard. Eastern-European Journal of Enterprise Technologies, 6(3 (96), 62–70.



Control processes