Automated software package for selecting transport technology for grain transportation by road freight transport

Vitalii Tsopa, Tamara Bilko, Serhiy Cheberyachko, Oleg Deryugin, Bohdan Hrymalo
Abstract

The aim of the work was to develop a software package for making a management decision to choose a transport technology for grain transportation by road from a set of alternatives. To achieve this goal, the morphological analysis method was used, which is based on selection of possible solutions for individual parts of the task. A software package for making a management decision from a set of alternatives was proposed, which consisted of four modules and four blocks. It was based on two methods: fuzzy Decision-Making Trial and Evaluation Laboratory (hereinafter – fuzzy Dematel) and gray relational analysis (hereinafter – GRA), which are used to determine the basic indicators and, on their basis, select the transport technology for grain transportation itself. The software package implemented an algorithm of eight steps: entering groups of indicators, their pairwise analysis, determining basic indicators based on the total matrix of relationships, entering the values of basic and reference indicators of transport technologies for grain transportation and weight coefficients, constructing gray analysis matrices, determining the index of basic indicators relative to target ones achievement. As a result, the best transport technology for grain freight road transportation was selected to meet the target (reference) indicators of the motor transport enterprise according to the following indicators: load transportation tariff; load transportation speed; diesel fuel consumption per 100 km; load volume; transportation distance; transport process energy intensity; time spent on loading and unloading operations; load safety during transportation; carrier reliability; driver satisfaction with working conditions. According to the calculation results, it was noted that the transport technology for grain transportation best meets the target indicators – load road train with dump semi-trailer. The dependences of the transport technology for load transportation basic indicators and motor transport enterprise target indicators were established, which allows improving the algorithm for making management decision to choose an effective transport technology for transportation from a set of alternative ones. The proposed process of selecting the best solution from a set of alternatives can be used in organisations that select the best technology for road load transportation of bulk cargo – grain

Keywords

transport technology, fuzzy Dematel method, gray relational analysis method, target indicators, benchmark indicators, management decisions

Suggested citation
Tsopa, V., Bilko, T., Cheberyachko, S., Deryugin, O., & Hrymalo, B. (2025). Automated software package for selecting transport technology for grain transportation by road freight transport. Machinery & Energetics, 16(2), 83-98. https://doi.org/10.31548/machinery/2.2025.83
References
  1. Basnak, P., Giesen, R., & Muñoz, J.C. (2020). Technology choices in public transport planning: A classification framework. Research in Transportation Economics, 83, article number 100901. doi: 10.1016/j.retrec.2020.100901.
  2. Bazaluk, O., Pavlychenko, A., Yavorska, O., Nesterova, O., Cheberiachko, S., Deryugin, O., & Lozynskyi, V. (2024). Improving the risk management process in quality management systems of higher education. Scientific Reports, 14, article number 3977. doi: 10.1038/s41598-024-53455-9.
  3. Cao, C., Su, Y., & Zheng, Q. (2021). Impact of policy incentives on technological innovation and diffusion within the new-energy vehicle industry: An ecosystem approach. Technology Analysis & Strategic Management. doi: 10.1080/09537325.2024.2306648.
  4. Čižiūnienė, K. (2022). Information systems in freight transportation. In Development of smart context-aware services for cargo transportation. International series in operations research & management science (vol. 330, pp. 285-299). Cham: Springer. doi: 10.1007/978-3-031-07199-7_13.
  5. Dulebenets, M.A. (2021). An adaptive polyploid memetic algorithm for scheduling trucks at a cross-docking terminal. Information Sciences, 565, 390-421. doi: 10.1016/j.ins.2021.02.039.
  6. Essghaier, F., Allaoui, H., & Goncalves, G. (2021). Truck to door assignment in a shared cross-dock under uncertainty. Expert Systems with Applications, 182, article number 114889. doi: 10.1016/j.eswa.2021.114889.
  7. European Commission. (n.d.). Identifying serious and complex ethics issues in EU-funded research. Horizon Europe guidelines. Retrieved from https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/guidelines-on-serious-and-complex-cases_he_en.pdf.
  8. Fausto, F., Reyna-Orta, A., Cuevas, E., Andrade, Á.G., & Perez-Cisneros, M. (2020). From ants to whales: Metaheuristics for all tastes. Artificial Intelligence Review, 53, 753-810. doi: 10.1007/s10462-018-09676-2.
  9. Feng, M., & Cheng, Y. (2021). Solving truck-cargo matching for drop-and-pull transport with genetic algorithm based on demand-capacity fitness. Alexandria Engineering Journal, 60(1), 61-72. doi: 10.1016/j.aej.2020.05.015.
  10. Guidance note of the European Commission on ethics and data protection. (2021). Retrieved from http://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/ethics-and-data-protection_he_en.pdf.
  11. Gustavo, C.B., Seimetz Chagas, R.D., Silva, V.A., Peres Vianna, I.G., Longhi, R.P., Ribas, P.C., & Ferreira Filho, V.J.M. (2021). A solution framework for the integrated problem of cargo assignment, fleet sizing, and delivery planning in offshore logistics. Computers & Industrial Engineering, 161, article number 107653. doi: 10.1016/j.cie.2021.107653.
  12. Hu, M., Wu, X., Yuan, Y., & Xu, C. (2024). Competitive analysis of heavy trucks with five types of fuels under different scenarios – a case study of China. Energies, 17(16), article number 3936. doi: 10.3390/en17163936.
  13. Hu, X., Guo, J., & Zhang, Y. (2019). Optimal strategies for the yard truck scheduling in container terminal with the consideration of container clusters. Computers & Industrial Engineering, 137, 106083. doi: 10.1016/j.cie.2019.106083
  14. Khanna, N., Lu, H., Fridley, D., & Zhou, N. (2021). Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050. Scientific Reports, 11(1), article number 20414. doi: 10.1038/s41598-021-99715-w.
  15. Khomenko, Yu., Matsiuk, V., Okorokov, A., & Gorobchenko, O. (2024). Development of a simulation model of grain delivery in global supply chains. Scientific Reports of the National University of Life and Environmental Sciences of Ukraine, 20(5), 21-35. doi: 10.31548/dopovidi/5.2024.21.
  16. Kim, H., & Kim, H. (2020). Integrated model for morphological analysis and named entity recognition based on label attention networks in Korean. Applied Sciences, 10(11), article number 3740. doi: 10.3390/app10113740.
  17. Kramchaninova, M., Kasatkina, M., & Masalova, T. (2021). Current state, issues and evolution horizons of railway cargo services in Ukraine. Problems and Prospects of Economics and Management, 3(15), 63-70.
  18. Lu, N., Liu, S., Du, J., Fang, Z., Dong, W., Tao, L., & Yang, Y. (2023). Grey relational analysis model with cross-sequences and its application in evaluating air quality index. Expert Systems with Applications, 233, article number 120910. doi: 10.1016/j.eswa.2023.120910.
  19. Lyu, Z., Pons, D., Chen, J., & Zhang, Y. (2022). Developing a stochastic two-tier architecture for modelling last-mile delivery and implementing in discrete-event simulation. Systems, 10(6), article number 214. doi: 10.3390/systems10060214.
  20. Muzylyov, D., Medvediev, I., & Pavlenko, O. (2024). Risk factor assessment in agricultural supply chain by fuzzy logic. IOP Conference Series: Earth and Environmental Science, 1376, article number 012038. doi: 10.1088/1755-1315/1376/1/012038.
  21. Nesterenko, H.I., Muzykin, M.I., Strelko, O.H., Bibik, S.I., & Aleksieieva, A.O. (2023). Analysis of possibilities for integrating the transport system of Ukraine into the European transport network. Systems and Technologies, 66(2), 97-107. doi: 10.32782/2521-6643-2023.2-66.11.
  22. Onopriienko, D. (2021). Management of support of sustainable development of the enterprise. Economics Finances Law, 6(1), 33-36. doi: 10.37634/efp.2021.6(1).7.
  23. Pečený, L., Meško, P., Kampf, R., & Gašparík, J. (2020). Optimisation in transport and logistic processes. Transportation Research Procedia, 44, 15-22. doi: 10.1016/j.trpro.2020.02.003
  24. Pu, Y.-H., Dejaegere, Q., Svensson, M., & Verhelst, S. (2024). Renewable methanol as a fuel for heavy-duty engines: A review of technologies enabling single-fuel solutions. Energies, 17(7), article number 1719. doi: 10.3390/en17071719.
  25. Quan, J., Bo, Z., & Dai, L. (2018). Green supplier selection for process industries using weighted grey incidence decision model. Complexity, 2018, article number 4631670. doi: 10.1155/2018/4631670.
  26. Scorrano, M., Danielis, R., & Giansoldati, M. (2020). Dissecting the total cost of ownership of fully electric cars in Italy: The impact of annual distance travelled, home charging and urban driving. Research in Transportation Economics, 80, article number 100799. doi: 10.1016/j.retrec.2019.100799.
  27. Sénquiz-Díaz, C. (2021). The effect of transport and logistics on trade facilitation and trade: A PLS-SEM approach. Economics, 9(2), 11-24. doi: 10.2478/eoik-2021-0021.
  28. Shatilo, O., Derevianko, O., Boichenko, K., Shevchuk, N., & Magdaliuk, O. (2023). Strategic development of motor transport enterprises’ innovative processes in Ukraine. Journal of Eastern European and Central Asian Research, 10(7), 940-955. doi: 10.15549/jeecar.v10i7.1326.
  29. Shmatko, D., Averyanov, V., Korobochka, A., & Sasov, A. (2021). Comprehensive solution to the problem of rolling stock choice and inventory management. Mathematical Modeling, 1(44), 76-82. doi: 10.31319/2519-8106.1(44)2021.235975.
  30. Skovron, I., Dorosh, A., Demchenko, Y., Bolvanovska, T., & Malashkin, V. (2020). The efficiency improvement of groupage cargo delivery by road transport. Transport Systems and Transportation Technologies, 20, 36-44. doi: 10.15802/tstt2020/217400.
  31. Škrinjarić, T. (2020). Dynamic portfolio optimization based on grey relational analysis approach. Expert Systems with Applications, 147, article number 113207. doi: 10.1016/j.eswa.2020.113207
  32. Škrinjarić, T., & Šego, B. (2019). Using grey incidence analysis approach in portfolio selection. International Journal of Financial Studies, 7(1), article number 1. doi: 10.3390/ijfs7010001.
  33. Touratier-Muller, N., & Jaussaud, J. (2021). Development of road freight transport indicators focused on sustainability to assist shippers: An analysis conducted in France through the FRET 21 Programme. Sustainability, 13(17), article number 9641. doi: 10.3390/su13179641.
  34. Tsopa, V., Cheberiachko, S., Yavorska, O., Deryugin, O., Litvinova, Y., & Lantukh, D. (2024a). Justification of the choice of measures to reduce logistics risks in the transportation of cargo. Proceedings of 28th international scientific conference “Transport means 2024” (pp. 547-552). Kaunas: Kaunas University of Technology. doi: 10.5755/e01.2351-7034.2024.
  35. Tsopa, V., Nehrii, T., Cheberiachko, S., Litvinova, Ya., Deryugin, O., & Horoshko, N. (2024b). Improving the risk assessment process of road accidents involving trucks. Transactions on Transport Sciences, 15(3), 4-11. doi: 10.5507/tots.2024.011.
  36. Vickerman, R. (2024). The transport problem: The need for consistent policies on pricing and investment. Transport Policy, 149, 49-58. doi: 10.1016/j.tranpol.2024.02.009.
  37. Wang, Q., Zhang, R., Lv, S., & Wang, Y. (2021). Open-pit mine truck fuel consumption pattern and application based on multi-dimensional features and XGBoost. Sustainable Energy Technologies and Assessments, 43, article number 100977. doi: 10.1016/j.seta.2020.100977.
  38. Yan, X., Zheng, W., Wei, Y., & Yan, Z. (2024). Current status and economic analysis of green hydrogen energy industry chain. Processes, 12(2), article number 315. doi: 10.3390/pr12020315.
  39. Zakeri, S., Chatterjee, P., Cheikhrouhou, N., & Konstantas, D. (2022). Ranking based on optimal points and win-loss-draw multi-criteria decision-making with application to supplier evaluation problem. Expert Systems with Applications, 191, article number 116258. doi: 10.1016/j.eswa.2021.116258.