Calculation of the parameters of the composite conveyor line with a constant speed of movement of subjects of labour
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- Category: Information technologies, systems analysis and administration
- Last Updated on 18 September 2018
- Published on 27 August 2018
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Authors:
О. М. Pihnastyi, Dr. Sc. (Tech.), Assoc. Prof., orcid.org/ 0000-0002-5424-9843, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
V. D. Khodusov, Dr. Sc. (Phys.-Math.), Prof., V. N. Karazin Kharkiv National University, Kharkiv, Ukraine
Abstract:
Purpose. Thedevelopment of analytical methods for calculating the parameters of a composite conveyor line using the models containing partial differential equations.
Methodology. To calculate the parameters of the conveyor line with a constant speed of movement of subjects of labour, the apparatus of mathematical physics is used.
Findings. The solution is given in an analytic form that specifies the state parameters of the production line for a given technology position as a function of time.
Originality. The scientific novelty of the results is the improvement of PDE-models of production systems of a conveyor type. The method for calculating the parameters of conveyor production, consisting of two connecting conveyor lines with a constant speed of movement of subjects of labour is offered. The considered method for calculation of conveyor production can be extended in case of a system with an arbitrary number of connecting conveyor lines.
Practical value. The practical significance lies in the fact that the proposed method for calculating the parameters of conveyor production can be used to design control systems for conveyor production with an arbitrary number of conveyor lines. An essential advantage of this method is that each conveyor line is described by a single partial differential equation, the solution to which is obtained analytically. Such a representation makes it possible to use solutions for predicting the state parameters of a production line.
References.
1. Armbruster, D., Ringhofer, C. and Jo, T-J., 2004. Continuous models for production flows. In: Proceedings of the 2004 American Control Conference. Boston, MA, USA, 2004, pp. 4589–4594. DOI: 10.23919/ACC.2004.1384034.
2. Schmitz, J. P., van Beek, D. A. and Rooda, J. E., 2002. Chaos in Discrete Production Systems. Journal of Manufacturing Systems, 21(3), pp. 236–246. DOI: 10.1016/S0278-6125(02)80164-9.
3. Law, A. M., 2015. Simulation Modeling and Analysis. McGraw-Hill.
4. Grosler, A., Thun, J. H. and Milling, P. M., 2008. System dynamics as a structural theory in operations management. Production and operations management, 17(3), pp. 373‒384. DOI: 10.3401/poms.1080.0023.
5. Pihnastyi, O. M., 2014. On a new class of dynamic models of conveyor lines of production systems. Nauchnye vedomosti Belgorodsko-go gosudarstvennogo universiteta, 31/1, pp. 147‒157, Available at: <https://goo.gl/IjFEsS> [Accessed 5 October 2017].
6. Armbruster, D., Marthaler, D. and Ringhofer, C., 2006. Kinetic and fluid model hierarchies for supply chains supporting policy attributes. Bulletin of the Institute of Mathematics. Academica Sinica, pp. 496–521. Available at: <https://math.la.asu.edu/~dieter/papers/due_date_paper_final_version.pdf> [Accessed 11 September 2017].
7. Demutskii, V. P., Pihnastaia, V. S. and Pihnastyi, O. M. 2005. Stochastic description of economic-indudtrial systems with large turnout. In: DopovіdіNacіonal›noїakademії nauk Ukraїni. Kiev: Vidavnichij dіm Akademperіodika, 7, pp. 66–71. Available at: <http://goo.gl/IhyOru> [Accessed 14 September 2017].
8. Lefeber, E., Berg, R. A. and Rooda, J. E., 2004. Modeling, Validation and Control of Manufacturing Systems. In: Proceeding of the 2004 American Control Conference, Massachusetts, 2004, pp. 4583–4588. DOI: 10.23919/ACC.2004.1384033.
9. Armbruster, D. A., Marthaler, D., Ringhofer, C., Kempf, K. and Jo, T-C., 2006. Continuum Model for a Re-entrant Factory. Operations research, 54(5), pp. 933–950. DOI: 10.1287/opre.1060.0321.
10. Tian, F., Willems, S. P. and Kempf, K. G., 2011. An iterative approach to item-level tactical production and inventory planning. International Journal of Production Economics, 133, pp. 439–450. DOI: 10.1016/j.ijpe.2010.07.011.
11. Armbruster, D., Degond, P. and Ringhofer, C., 2006. A model for the dynamics of large queuing networks and supply chains. SIAM Journal on Applied Mathematics, 83, pp. 896–920. DOI: 10.1137/040604625.
12. Demutskii, V. P., Pihnastaia, V. S. and Pihnastyi, O. M., 2003. Enterprise theory: Stability of mass production functioning and promoting products into the market. Har’kov.: HNU, 272 p. DOI: 10.13140/RG.2.1.5018.7123.
13. Halepoto, I. A., Degond, P. and Ringhofer, C., 2016. Design and Implementation of Intelligent Energy Efficient Conveyor System Based on Variable Speed Drive Model. Control and Physical Modeling International Journal of Control and Automation, 9(6), pp. 379‒388. DOI: 10.14257/ijca.2016.9.6.36.
14. Hiltermann, J., Lodewijks, G., Schott, D. L., Rijsenbrij, J. C., Dekkers, J. and Pang, Y., 2011. A methodology to predict power savings of troughed belt conveyors by speed control. Particulate science and technology, 29(1), pp. 14–27. DOI: 10.1080/02726351.2010.491105.
15. Lauhoff, H., 2005. Speed Control on Belt Conveyors – Does it Really Save Energy? Bulk Solids Handling Publ., 25(6), pp. 368‒377. Available at: <https://goo.gl/PJdY8x> [Accessed 14 May 2017].
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