Skip to main content

Mathematical Modeling as a Tool for Selecting a Rational Logistical Route in Multimodal Transport Systems

  • Chapter
  • First Online:
Industry 4.0 Challenges in Smart Cities

Abstract

The paper aims to carry out the mathematical modeling of the goods delivery process from China to Ukraine, using possible organization options of such work and advantages of existing routes using different kinds of transport. The paper presented successful mathematical modeling of goods delivery from China to Ukraine to determine effective options. The structure of goods delivery from China to Ukraine has been designed in the form of four alternative routes (options), which considering the use of railway, maritime, road and air transport, and related infrastructure (stations, ports, warehouses, terminals, customs). It has been found that values of order delivery volumes of the corresponding type of goods based on parameter analysis of orders flow for trade enterprises of Kharkiv, cargo transportation volume in the current batch, risk assessment factor using related kinds of transport, and goods delivery time for each option. As an experiment result, enterprises' profit was obtained using initial and final values of the unit of the good according to proposed options. It was taken into account in regression model designing, which allowed determining the best route of transportation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kirichenko AI (2012) The application of information technologies in the management of cargo delivery processes. Transport problems: collection of scientific papers. vol. 9, NTU, pp 17–27

    Google Scholar 

  2. Zelikov VA, Akopova ES, Pilivanova EK, Popova LK (2019) Model of management of the risk component of intermodal transport: information and communication technologies of transport logistics. Perspectives on the use of new information and communication technology (ICT) in the modern economy. ISC 2017. Advances in intelligent systems and computing, vol 726. Springer, Cham

    Google Scholar 

  3. Order of the Cabinet of Ministers of Ukraine on transport strategy 2030. https://www.kmu.gov.ua

  4. Geographical structure of foreign trade in goods in January–July 2019. http://www.ukrstat.gov.ua/operativ/operativ2019/zd/ztt/ztt_u/ztt0719_u.htm

  5. Omelianenko S, Kondratenko Y, Kondratenko G, Sidenko I (2019) Advanced system of planning and optimization of cargo delivery and its IoT application. In: 3rd international conference on advanced information and communications technologies (AICT). AICT, Lviv, pp 302–307. https://doi.org/10.1109/AIACT.2019.8847744

    Chapter  Google Scholar 

  6. Peraković D, Periša M, Sente RE (2019) Information and communication technologies within industry 4.0 concept. In: Ivanov V et al (eds) Advances in design, simulation and manufacturing. DSMIE-2018, Lecture notes in mechanical engineering. Springer, Cham, pp 127–134. https://doi.org/10.1007/978-3-319-93587-4_14

    Chapter  Google Scholar 

  7. Karabegović I, Turmanidze R, Dašić P (2020) Robotics and automation as a foundation of the fourth industrial revolution - industry 4.0. In: Tonkonogyi V et al (eds) Advanced manufacturing processes. InterPartner-2019, Lecture notes in mechanical engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-40724-7_13

    Chapter  Google Scholar 

  8. Fernando E, Surjandy, Warnars HLHS, Meyliana, Kosala R, Abdrachman E (2018) Critical success factor of information technology implementation in supply chain management. In: Literature review 5th international conference on information technology, computer, and electrical engineering (ICITACEE). IEEE, Piscataway. https://doi.org/10.1109/ICITACEE.2018.8576979

    Chapter  Google Scholar 

  9. Shramenko N, Muzylyov D, Shramenko V (2020) Model for choosing rational technology of containers transshipment in multimodal cargo delivery systems. In: Karabegović I (ed) New technologies, development and application III. NT 2020, Lecture notes in networks and systems. Springer, Cham, pp 621–629. https://doi.org/10.1007/978-3-030-46817-0_72

    Chapter  Google Scholar 

  10. Volkov V, Taran I, Volkova T, Pavlenko O, Berezhnaja N (2020) Determining the efficient management system for a specialized transport enterprise. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 4:185–191

    Article  Google Scholar 

  11. Konovalenko I, Ludwig A (2019) Event processing in supply chain management – the status quo and research outlook. Comput Ind 105:229–249. https://doi.org/10.1016/j.compind.2018.12.009

    Article  Google Scholar 

  12. Borgi T, Zoghlami N, Abed M, Saber Naceur M (2017) Big data for operational efficiency of transport and logistics: a review. In: 6th IEEE international conference on advanced logistics and transport (ICALT). https://doi.org/10.1109/ICAdLT.2017.8547029

    Chapter  Google Scholar 

  13. Delivery of cargo from China. https://sigma-logistics.com.ua/services

  14. Delivery of goods by rail from China. https://fialan.ua/services/gd-dostavka-iz-kitaya/

  15. Delivery of cargo by air. https://www.china-cargo.in.ua

  16. Seo Y, Chen F, Roh SY (2017) Multimodal transportation: the case of laptop from Chongqing in China to Rotterdam in Europe. Asian J Ship Log 33(3):155–165. https://doi.org/10.1016/j.ajsl.2017.09.005

    Article  Google Scholar 

  17. Kundu T, Sheu J-B (2019) Analyzing the effect of government subsidy on shippers’ mode switching behavior in the belt and road strategic context. Transp Res E 129:175–202. https://doi.org/10.1016/j.tre.2019.08.007

    Article  Google Scholar 

  18. Liu X, Zhang K, Chen B, Zhou J, Miao L (2018) Analysis of logistics service supply chain for the one belt and one road initiative of China. Transp Res E 117:23–39. https://doi.org/10.1016/j.tre.2018.01.019

    Article  Google Scholar 

  19. Jin CF, Yang HM, Ling L (2010) Wang research on optimization and debugging simulation model of logistics center based on neural network. Appl Mech Mater 38:1060–1063. https://doi.org/10.4028/www.scientific.net/AMM.37-38.1060

    Article  Google Scholar 

  20. He W, Lu T, Yu CQ (2014) A novel traffic flow forecasting method based on the artificial neural networks and intelligent transportation systems data mining. Adv Mater Res 842:708–711. https://doi.org/10.4028/www.scientific.net/AMR.842.708

    Article  Google Scholar 

  21. Subbotin SO, Oliynyk AO (2014) Neural networks: teach. Manual. ZNTU, Zaporizhzhya

    Google Scholar 

  22. Muzylyov D, Shramenko N (2020) Blockchain technology in transportation as a part of the efficiency in industry 4.0 strategy. In: Tonkonogyi V et al (eds) Advanced manufacturing processes. InterPartner-2019, Lecture notes in mechanical engineering. Springer, Cham, pp 216–225. https://doi.org/10.1007/978-3-030-40724-7_22

    Chapter  Google Scholar 

  23. Wang L, Zhu XN, Xie ZY (2011) Object-oriented petri net modeling and analysis of China railway container freight yard logistic system. Key Eng Mater 467:990–995. https://doi.org/10.4028/www.scientific.net/KEM.467-469.990

    Article  Google Scholar 

  24. Shramenko N, Pavlenko O, Muzylyov D (2020) Logistics optimization of agricultural products supply to the European union based on modeling by petri nets. In: Karabegović I (ed) New technologies, development and application III, Lecture notes in networks and systems. Springer, Cham, pp 596–604. https://doi.org/10.1007/978-3-030-46817-0_69

    Chapter  Google Scholar 

  25. Zhong WZ, Fu XQ, Wang YP (2013) Petri net modeling: container terminal production operation processing system analysis. Appl Mech Mater 409:1320–1324. https://doi.org/10.4028/www.scientific.net/AMM.409-410.1320

    Article  Google Scholar 

  26. Pavlenko O, Velykodnyi D, Lavrentieva O, Filatov S (2020) The procedures of logistic transport systems simulation into the petri nets environment. CEUR Workshop Proc 2732:854–868

    Google Scholar 

  27. Rossolov A, Kopytkov D, Kush Y, Zadorozhna V (2017) Research of effectiveness of unimodal and multimodal transportation involving land modes of transport. Eastern-Eur J Enterpr Technol 5(89):60–69. https://doi.org/10.15587/1729-4061.2017.112356

    Article  Google Scholar 

  28. Turpak SM, Taran IO, Fomin OV, Tretiak OO (2018) Logistic technology to deliver raw material for metallurgical production. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 1:162–169. https://doi.org/10.29202/nvngu/2018-1/3

    Article  Google Scholar 

  29. Litvinova Y, Nosal-Hoy K, Solecka K, Taran I (2020) Improvement of efficiency of processes of mining product processing at transport hubs. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 1:141–145. https://doi.org/10.33271/nvngu/2020-1/141

    Article  Google Scholar 

  30. Luscinski S, Ivanov VA (2020) Simulation study of industry 4.0 factories based on the ontology on flexibility with using FlexSim software. Manage Prod Eng Rev 11(3):74–83. https://doi.org/10.24425/mper.2020.134934

    Article  Google Scholar 

  31. Medvediev I, Muzylyov D, Shramenko N, Nosko P, Eliseyev P, Ivanov V (2020) Design logical linguistic models to calculate necessity in trucks during agricultural cargoes logistics using fuzzy logic. Acta Logist 7(3):155–166. https://doi.org/10.22306/al.v7i3.165

    Article  Google Scholar 

  32. Steelant J (2012) Pioneering in hypersonic transportation: long term perspectives and technological challenges. In: Kontis K (ed) 28th international symposium on shock waves. Springer, Berlin, pp 39–43. https://doi.org/10.1007/978-3-642-25688-2_6

    Chapter  Google Scholar 

  33. Silva JB, Giannotti MA, Larocca APC et al (2017) Towards a spatial data infrastructure for technological disasters: an approach for the road transportation of hazardous materials. GeoJournal 82:293–310. https://doi.org/10.1007/s10708-015-9680-0

    Article  Google Scholar 

  34. Mieczkowski B (1980) Technological change in transportation in Eastern Europe. In: Mieczkowski B (ed) East European transport regions and modes. Developments in transport studies. Springer, Dordrecht, pp 282–316. https://doi.org/10.1007/978-94-009-8899-6_13

    Chapter  Google Scholar 

  35. Medvediev I, Sakno O, Moisia D, Kolesnikova T, Rogovyi A (2020) Linear and non-linear wheel slip hypothesis in studying stationary modes of a double road train. In: Proceedings 2020 IEEE 15th international conference on computer sciences and information technologies (CSIT), vol 1. IEEE, Piscataway, pp 183–187

    Google Scholar 

  36. Kliuiev S, Medvediev I, Soroka S, Dubuk V (2020) Development of the intelligent rail vehicle control system. In: Proceedings 2020 IEEE 15th international conference on computer sciences and information technologies (CSIT), vol 1. IEEE, Piscataway, pp 369–372

    Google Scholar 

  37. Vojtov V, Kutiya O, Berezhnaja N, Karnaukh N, Belyaeva O (2019) Modeling of reliability of logistic systems of urban freight transportation taking into account stream loading. Eastern-Eur J Enterpr Technol 7(4):15–21. https://doi.org/10.15587/1729-4061.2019.175064

    Article  Google Scholar 

  38. Grabis J, Haidabrus B, Protsenko S, Protsenko I, Rovna A (2019) Data science approach for it project management. Vide Tehnol Res 2:51–55. https://doi.org/10.17770/etr2019vol2.4163

    Article  Google Scholar 

  39. Dobrotvorskiy S, Basova Y, Dobrovolska L, Sokol Y, Kazantsev N (2020) Big challenges of small manufacturing enterprises in industry 4.0. In: Ivanov V, Trojanowska J, Pavlenko I, Zajac J, Peraković D (eds) Advances in design, simulation and manufacturing III, vol 1. IEEE, Piscataway, pp 118–127. https://doi.org/10.1007/978-3-030-50794-7_12

    Chapter  Google Scholar 

  40. Li F, Zhu YP, Wu HR (2013) Modeling and optimization of traceability system for agriculture products supply chain. Adv Mater Res 605:574–579. https://doi.org/10.4028/www.scientific.net/AMR.605-607.574

    Article  Google Scholar 

  41. Qu JH, Yao XS, Ying JL (2012) Agricultural products logistics operational pattern based on information center. Adv Mater Res 363:1679–1683. https://doi.org/10.4028/www.scientific.net/AMR.361-363.1679

    Article  Google Scholar 

  42. Vendrell-Herrero F, Bustinza OF, Parry G, Georgantzis N (2017) Servitization, digitization and supply chain interdependency. Ind Mark Manag 60:69–81. https://doi.org/10.1016/j.indmarman.2016.06.013

    Article  Google Scholar 

  43. Xue L, Zhang C, Ling H, Zhao X (2013) Risk mitigation in supply chain digitization: system modularity and information technology governance. J Manag Inf Syst 30:325–352. https://doi.org/10.2753/MIS0742-1222300110

    Article  Google Scholar 

  44. Shramenko N, Muzylyov D, Shramenko V (2020) Methodology of costs assessment for customer transportation service of small perishable cargoes. Int J Bus Perform Manag 21(2):132–148. https://doi.org/10.1504/IJBPM.2020.10027632

    Article  Google Scholar 

  45. Muzylyov D, Shramenko N (2020) Mathematical model of reverse loading advisability for trucks considering idle times. In: Karabegović I (ed) New technologies, development and application III. NT 2020. Lecture notes in networks and systems, vol 128. Springer, Cham, pp 612–620. https://doi.org/10.1007/978-3-030-46817-0_71

    Chapter  Google Scholar 

  46. Muzylyov D, Shramenko N, Shramenko V (2020) Integrated business-criterion to choose a rational supply chain for perishable agricultural goods at automobile transportations. Int J Bus Perform Manag 21(2):166–183. https://doi.org/10.1504/IJBPM.2020.10027634

    Article  Google Scholar 

  47. Chinese imports: how Ukrainian businesses protect their interests. https://biz.liga.net/ekonomika/all/opinion/kitayskiy-import-kak-ukrainskomu-biznesu-zaschitit-svoi-interesy

  48. China has become the largest business partner of Ukraine - infographic. https://nv.ua/biz/economics/torgovlya-s-kitaem-kitay-stal-glavnym-delovym-partnerom-ukrainy-novosti-ukrainy-50048806.html

  49. Ukraine-China. Colonial imbalance. https://tyzhden.ua/Economics/233713

  50. Building the silk road. http://investasianmain.gelderbauerltd.netdna-cdn.com/wp-content/uploads/2015/02/MapChinaNewSilkRoad.jpg

Download references

Acknowledgments

This paper has been written with the support of the H2020 project “A Policy Tool Kit for the Promotion of Intercultural Competence and Diversity Beliefs, Reduction of Discrimination and Integration of Migrants into the Labor Market”, acronym FairFuture, Nr. 870307.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dagmar Cagáňová .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pavlenko, O., Muzylyov, D., Shramenko, N., Cagáňová, D., Ivanov, V. (2023). Mathematical Modeling as a Tool for Selecting a Rational Logistical Route in Multimodal Transport Systems. In: Cagáňová, D., Horňáková, N. (eds) Industry 4.0 Challenges in Smart Cities. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-92968-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-92968-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92967-1

  • Online ISBN: 978-3-030-92968-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics