Management of technological process optimisation

• Autorzy: Lypchuk Vasyl , Dmytriv Vasyl

Identyfikatory

DOI

10.2478/emj-2020-0022

• Języki publikacji: EN

Abstrakty

EN

The research aims to characterise the optimisation of a technological process depending on the main time parameters for production. The optimisation does not require to correct technical parameters of a system, but rather the organisational and managerial factors of the technological process. The workload is taken as an evaluation criterion, which factors in the probability distribution of time characteristics of computer process operations. Time characteristics that represent the performance of an operation influence the workloads of an operator and equipment, determining the productivity of the technological process. Analytical models were developed for the operational control of a production line efficiency considering the probability–statistical parameters pertaining to the performance of operations and technological equipment peculiarities. The article presents research results, which characterise the dependence of a production line efficiency on the type of equipment, and the duration of preparatory and final operations considering their probability. Under an optimal workload of the operator, the duration of the complete program changes linearly, regardless of the time required for the performance of operations by a computer without the involvement of the operator, and depending on the type of equipment. A managerial decision can be optimal under the condition that the factor of technological process efficiency (K_TP) tends to max. The developed method of analytical determination can be used to calculate the workload of both an operator and technological equipment. The calculations of the duration of a production line operation resulted in the methodology for the consideration of probability characteristics pertaining to the time distribution of the period required to perform operations, which influences the unequal efficiency of the production line. The probabilistic character of time distribution related to intervals of performed operations serves as a parameter in the management of technological process optimisation, which can be achieved using simulators of technological processes optimised in terms of their efficiency.

Słowa kluczowe

EN

workload factor; process productivity; analytical model; duration of operations; amount of equipment

PL

współczynnik obciążenia pracą; produktywność procesu; model analityczny

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2020
• Tom: Vol. 12, no. 3
• Strony: 103--115
• Opis fizyczny: Bibliogr. 21 poz., tab., wykr.

Twórcy

autor

Lypchuk Vasyl

• Kielce University of Technology, Poland

autor

Dmytriv Vasyl

• Lviv Polytechnic National University, Ukraine

Bibliografia

• Åkesson, J. (2008). Optimica - An Extension of Modelica Supporting Dynamic Optimization. Paper presented at 6th International Modelica Conference, Bielefeld, Germany, 57-66. Retrieved from https:// portal.research.lu.se/portal/files/6062979/8253707.pdf
• Al-Ahmari, A., Abidi, M., Ahmad, A., & Darmoul, S. (2016). Development of a virtual manufacturing assembly simulation system. Advances in Mechanical Engineering, 8(3), 1-13. doi: 10.1177/1687814016639824.
• Briesemeister, R., & Novaes, А. (2017). Comparing an approximate queuing approach with simulation for the solution of a cross-docking problem. Journal of Applied Mathematics, 6, 1-11. doi: 10.1155/2017/4987127
• Bronstein, I. N., & Semendiaiev, K. A. (1986). Guide on mathematics for engineers and students of higher education establishments. Moscow, Russia: Nauka.
• Dmytriv, V., Dmytriv, I., Lavryk, Y., & Horodeckyy, I. (2018). Models of adaptation of the milking machines systems. Contemporary Research Trends in Agricultural Engineering. BIO Web of Conferences, 10, 02004. doi:10.1051/bioconf/20181002004
• Gálová, K., Rajnoha, R., & Ondra, P. (2018). The Use Of Industrial Lean Management Methods In The Economics Practice: An Empirical Study Of The Production Companies In The Czech Republic. Polish Journal of Management Studies, 17(1), 93-104.
• García, S. G., & García, M. G. (2018). Design and Simulation of Production and Maintenance Management Applying the Viable System Model: The Case of an OEM Plant. Materials, 11, 1346. doi:10.3390/ ma11081346
• Ivanov, D. (2017). Operations and supply chain simulation with AnyLogic. Decision-oriented introductory notes for master students. Berlin School of Economics and Law. Retrieved from https://www.anylogic.com/ upload/pdf/Ivanov_AL_book_2017.pdf
• Kibira, D., & Shao, G. (2016). Virtual Factory Framework for Supporting Production Planning and Control. Conference Paper in IFIP Advances in Information and Communication Technology. doi: 10.1007/978- 3-319-51133-7_35
• Kikolski M. & Ko C-H. (2018). Facility layout design – review of current research directions. Engineering Management in Production and Services, 10(3), 70- 79. doi: 10.2478/emj-2018-0018
• Kodra, Yu. V., Stotsko, Z. A., & Havrylchenko, O. V. (2008). Loading devices of technological machines. Calculation and construction. Manual. Lviv, Ukraine: Publishing house “Beskyd Bit”.
• Mourtzis, D. (2019). Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949. doi: 10.1080/00207543.2019.1636321
• Mourtzis, D., Papakostas, N., Mavrikios, D., Makris, S., & Alexopoulos, K. (2015). The Role of Simulation In Digital Manufacturing – Applications And Outlook. International Journal of Computer Integrated Manufacturing, 28(1), 3-24. doi: 10.1080/0951192X.2013.800234
• Rahman, Ch., & Ullah, S. (2015). Process Flow Improvement Proposal of a Batch Manufacturing System Using ARENA Simulation Modeling. Review of General Management, 21(1), 63-77.
• Ran, L., Xiaolei, X., Kaiye, Y., & Qiaoyu, H. (2018). A survey on simulation optimization for the manufacturing system operation. International Journal of Modelling and Simulation, 38(2), 116-127. doi: 10.1080/022 86203.2017.1401418
• Shao, G., Brodsky, A., Shin, S.-J., & Kim, D. B. (2014). Decision guidance methodology for sustainable manufacturing using process analytics formalism. Journal of Intelligent Manufacturing, 28(2), 455-472. doi: 10.1007/s10845-014-0995-3
• Sobolewski, J. (red.), Sieminski, P., & Sobieszczanski, J. (2012). Technologies of production and projection of technological processes. Warsaw, Poland: Politechnika Warszawska.
• Sujová, E., Cierna, H., & Bambura, R. (2019). Simulation model of production as tool for industry 4.0 implementation into practice. 18-th International Scientific Conference Engineering for Rural Development. doi: 10.22616/ERDev2019.18.N279
• Tkaczyk, S., & Roszak, M. (2002). Analysis of the technological process based on the added value criterion. 11 International Conference AMME’2002, Gliwice-Zakopane. Retrieved from http://www.journalamme. org/ papers_ amme02/11121 .pdf
• Wędrychowicz, M., & Bydałek, A. (2017). Optimization of the production process of “Glogow II” melting with application of technological supplement CaF2. Methods and Instruments of Production Technique, 2, 123- 132.
• Zwierzyński, P., & Ahmad, H. (2018). Seru production as an alternative to a traditional assembly line. Engineering Management in Production and Services, 10(3), 62- 69. doi: 10.2478/emj-2018-0017