The study of the operation of quarry vehicles made it possible to form the target function of the study, taking into account the criterion of the efficiency of all processes of the system, which provides for the reduction of costs for the operation of the transport and production system of the quarry of a metallurgical enterprise, namely, the subsystems: “Incoming raw materials”, “Processing of raw materials”, “Sales of raw materials”. Factors influencing the cost indicator are highlighted. These factors include the production downtime of motor vehicles, the speed of motor vehicles with cargo, and the speed of motor vehicles without cargo. The values of these factors were obtained in result of timing the operation of motor vehicles on technological routes for four days. The levels of variation intervals and the nature of their changes for the three regimes were calculated for each of the subsystems. A regression analysis of the investigated factors was carried out to model the costs. The response surfaces of the obtained mathematical models are constructed, namely: the influence of the production downtime of motor vehicles and the speed of movement without cargo on the costs of functioning of subsystems, the influence of production downtime of motor vehicles and the speed of movement with cargo on the costs of the functioning of subsystems, the influence of the speed of movement with cargo and speed of movement without cargo for the costs of functioning of subsystems. The optimal values for reducing the cost of functioning of the “Processing of raw materials” subsystem are the value of production downtime – 4-5 min., the speed of motor vehicles without cargo – 9 min., and the speed of motor vehicles with cargo – 9 km/h. The optimal values for reducing the cost of functioning of the “Sales of raw materials” subsystem are the value of production downtime – 4-6 min., the speed of motor vehicles without cargo – 14-16 min., and the speed of motor vehicles with cargo – 13-15 km/h. The optimal values for reducing the cost of functioning of the subsystem “Incoming raw materials” are: the value of production downtime is 4-5 minutes, the speed of motor vehicles without cargo is 7-8 km/h, the speed of motor vehicles with cargo is 10 km/h.
1. Chislov, O., Bogachev, V., Zadorozhniy, V., Kravets, A., Bakalov, M., & Bogachev, T. (2021). Mathematical modeling of cargo flow distribution in a regional multimodal transportation system. Transport Problems, 16(2), 153-165. doi: 10.21307/tp-2021-031 (in English).
https://doi.org/10.21307/tp-2021-031
2. Jahed, A., Tavakkoli Moghaddam, R. (2021). Mathematical modeling for a flexible manufacturing scheduling problem in an intelligent transportation system. Iranian Journal of Management Studies, 14(1), 189-208. doi: 10.22059/ijms.2020.261618.673203 (in English).
3. Pavlenko, O., Velykodnyi, D., Lavrentieva, O., Filatov, S. (2020). The Procedures of Logistic Transport Systems Simulation in the Petri Nets Environment. In Ceur workshop proceedings, 2732, (pp. 854-68). (in English).
4. Barykin, S. Y., Kapustina, I. V., Sergeev, S. M., & Yadykin, V. K. (2020). Algorithmic foundations of economic and mathematical modeling of network logistics processes. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 189. doi: 10.3390/joitmc6040189 (in English).
https://doi.org/10.3390/joitmc6040189
5. Tavasszy, L. A. (2020). Predicting the effects of logistics innovations on freight systems: Directions for research. Transport Policy, 86, A1-A6. doi: 10.1016/j.tranpol.2019.11.004 (in English).
https://doi.org/10.1016/j.tranpol.2019.11.004
6. de la Torre, R., Corlu, C. G., Faulin, J., Onggo, B. S., & Juan, A. A. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: models and applications. Sustainability, 13(3), 1551. doi: 10.3390/su13031551 (in English).
https://doi.org/10.3390/su13031551
7. Zhang, Y., Kou, X., Song, Z., Fan, Y., Usman, M., & Jagota, V. (2022). Research on logistics management layout optimization and real-time application based on nonlinear programming. Nonlinear Engineering, 10(1), 526-534. doi: 10.1515/nleng-2021-0043 (in English).
https://doi.org/10.1515/nleng-2021-0043
8. Bučková, M., Skokan, R., Fusko, M., & Hodoň, R. (2019). Designing of logistics systems with using of computer simulation and emulation. Transportation Research Procedia, 40, 978-985. doi: 10.1016/j.trpro.2019.07.137 (in English).
https://doi.org/10.1016/j.trpro.2019.07.137
9. Yazdani, M., Pamucar, D., Chatterjee, P., Chakraborty, S. (2020). Development of a decision support framework for sustainable freight transport system evaluation using rough numbers. International Journal of Production Research, 58(14), 4325-4351. doi: 10.1080/00207543.2019.1651945 (in English).
https://doi.org/10.1080/00207543.2019.1651945
10. Devendra K. P., Lakshman S. Thakur, Shams R. (2019) Performance evaluation framework for sustainable freight transportation systems, International Journal of Production Research, 57(19), 6202-6222. doi: 10.1080/00207543.2019.1602741 (in English).
https://doi.org/10.1080/00207543.2019.1602741
11. Nassim M., Nadia H., Laurent D. (2021) Sustainable Freight Transport Association. Journal of the Society for Operational Research, 72 (10), 2180-2195. (in English).
https://doi.org/10.1080/01605682.2020.1772022
12. Guo, Z., Zhang, Y., Zhao, X., Song, X. (2020). CPS-based self-adaptive collaborative control for smart production-logistics systems. IEEE Transactions on Cybernetics, 51(1), 188-198. doi: 10.1109/TCYB.2020.2964301 (in English).
https://doi.org/10.1109/TCYB.2020.2964301
13. Kernychniy, B., & Radynskiy, S. (2021). Metodychnyi instrumentarii otsiniuvannia efektyvnosti upravlinnia transportno-lohistychnym obsluhovuvanniam promyslovoho pidpryiemstva [Methodical tools for evaluating the effectiveness of transport and logistics services management of an industrial enterprise]. Innovative Solution in Modern Science, 7(43), 169-191. doi: 10.26886/2414-634X.7(43)2020.11 (in Ukrainian).
https://doi.org/10.26886/2414-634X.7(43)2020.11
14. Shramenko, N., & Muzylyov, D. (2020). Forecasting of overloading volumes in transport systems based on the fuzzy-neural model. In Advances in Design, Simulation and Manufacturing II: Proceedings of the 2nd International Conference on Design, Simulation, Manufacturing: The Innovation Exchange, DSMIE-2019. (pp. 311-320). (in English).
https://doi.org/10.1007/978-3-030-22365-6_31