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Published December 31, 2020 | Version v1
Journal article Open

Development of a multimodal (railroad-water) chain of grain supply by the agent-based simulation method

Creators

  • 1. Kyiv National University of Trade and Economics
  • 2. State University of Infrastructure and Technology

Description

The results of the simulation of a multi-element chain of grain supply by the rail and water multimodal route were shown. Mathematical substantiation of the optimization problem was presented. The minimum cargo delivery time was selected as the optimization criterion. The limits for the admissible use (loading) of fleets of transport units of railroad and water transport were selected as optimization constraints. The optimization model is a multi-parametric problem of stochastic programming. The objective function of the model was represented in implicit expression. The search for the solution of the optimization model was performed using experiments with the developed simulation model.

The simulation model is based on the discrete-event and agent-based principles, it simulates the interaction of two railroad and one sea transport and technological lines, as well as terminal points of accumulation, storage, and reloading of cargo batches. One ton of wheat grain acts as a part of the cargo module.

The simulation model was developed in AnyLogic RE (USA) and Java SE (USA) environments. The algorithm of the simulation model involves the interaction of populations of agents of transport junction points; agents of transport and technological lines; populations of agents of fleets of transport units; agents of information orders for transportation. The model was implemented using the example of the actual process of grain supply from Ukraine to Egypt.

The model was studied using the integer optimization method. As a result of experiments, the optimal values of the required stock of cars, locomotives, and naval vessels were established. In addition, the required capacity of granaries at the shipping stations and seaports’ terminals, as well as the necessary capacity of track development of railroad stations, were found. The established average delivery time was within 185 hours

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References

  • De Bok, M., Tavasszy, L., Thoen, S. (2020). Application of an empirical multi-agent model for urban goods transport to analyze impacts of zero emission zones in The Netherlands. Transport Policy. doi: https://doi.org/10.1016/j.tranpol.2020.07.010
  • Ziemke, D., Kaddoura, I., Nagel, K. (2019). The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science, 151, 870–877. doi: https://doi.org/10.1016/j.procs.2019.04.120
  • Llorca, C., Kuehnel, N., Moeckel, R. (2020). Agent-based integrated land use/transport models: a study on scale factors and transport model simulation intervals. Procedia Computer Science, 170, 733–738. doi: https://doi.org/10.1016/j.procs.2020.03.163
  • Leng, N., Corman, F. (2020). How the issue time of information affects passengers in public transport disruptions: an agent-based simulation approach. Procedia Computer Science, 170, 382–389. doi: https://doi.org/10.1016/j.procs.2020.03.068
  • Müller, S. A., Leich, G., Nagel, K. (2020). The effect of unexpected disruptions and information times on public transport passengers: a simulation study. Procedia Computer Science, 170, 745–750. doi: https://doi.org/10.1016/j.procs.2020.03.161
  • Calabrò, G., Inturri, G., Pira, M. L., Pluchino, A., Ignaccolo, M. (2020). Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization. Transportation Research Procedia, 45, 234–241. doi: https://doi.org/10.1016/j.trpro.2020.03.012
  • Lee, E., Zaman Patwary, A. U., Huang, W., Lo, H. K. (2020). Transit interchange discount optimization using an agent-based simulation model. Procedia Computer Science, 170, 702–707. doi: https://doi.org/10.1016/j.procs.2020.03.168
  • Hebenstreit, C., Fellendorf, M. (2018). A dynamic bike sharing module for agent-based transport simulation, within multimodal context. Procedia Computer Science, 130, 65–72. doi: https://doi.org/10.1016/j.procs.2018.04.013
  • Sommerfeld, D., Teucke, M., Freitag, M. (2018). Identification of Sensor Requirements for a Quality Data-based Risk Management in Multimodal Supply Chains. Procedia CIRP, 72, 563–568. doi: https://doi.org/10.1016/j.procir.2018.03.193
  • Kagho, G. O., Balac, M., Axhausen, K. W. (2020). Agent-Based Models in Transport Planning: Current State, Issues, and Expectations. Procedia Computer Science, 170, 726–732. doi: https://doi.org/10.1016/j.procs.2020.03.164
  • Anda, C., Ordonez Medina, S. A., Fourie, P. (2018). Multi-agent urban transport simulations using OD matrices from mobile phone data. Procedia Computer Science, 130, 803–809. doi: https://doi.org/10.1016/j.procs.2018.04.139
  • Thunig, T., Kühnel, N., Nagel, K. (2019). Adaptive traffic signal control for real-world scenarios in agent-based transport simulations. Transportation Research Procedia, 37, 481–488. doi: https://doi.org/10.1016/j.trpro.2018.12.215
  • Shen, Y., Guo, Y., Chen, W. (2019). Safety analysis of China's marine energy channel based on Multi - Agent simulation. Energy Procedia, 158, 3259–3264. doi: https://doi.org/10.1016/j.egypro.2019.01.988
  • Rogeberg, O. (2019). A meta-analysis of the crash risk of cannabis-positive drivers in culpability studies – Avoiding interpretational bias. Accident Analysis & Prevention, 123, 69–78. doi: https://doi.org/10.1016/j.aap.2018.11.011
  • Samsonkin, V., Goretskyi, O., Matsiuk, V., Myronenko, V., Boynik, A., Merkulov, V. (2019). Development of an approach for operative control over railway transport technological safety based on the identification of risks in the indicators of its operation. Eastern-European Journal of Enterprise Technologies, 6 (3 (102)), 6–14. doi: https://doi.org/10.15587/1729-4061.2019.184162
  • Mazaraki, A. A., Boiko, M. H., Bosovska, M. V., Kulyk, M. V. (2020). Multi-agent information service system of managing integration processes of enterprises. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 3, 103–108. doi: https://doi.org/10.33271/nvngu/2020-3/103
  • Ilchenko, N., Kulik, A., Magda, R. (2018). Trends in development of wholesale trade in Ukraine. Economic Annals-ХХI, 170 (3-4), 38–42. doi: https://doi.org/10.21003/ea.v170-07
  • Bučková, M., Krajčovič, M., Edl, M. (2017). Computer Simulation and Optimization of Transport Distances of Order Picking Processes. Procedia Engineering, 192, 69–74. doi: https://doi.org/10.1016/j.proeng.2017.06.012
  • Prokhorchenko, А., Parkhomenko, L., Kyman, A., Matsiuk, V., Stepanova, J. (2019). Improvement of the technology of accelerated passage of low-capacity car traffic on the basis of scheduling of grouped trains of operational purpose. Procedia Computer Science, 149, 86–94. doi: https://doi.org/10.1016/j.procs.2019.01.111
  • Matsiuk, V., Myronenko, V., Horoshko, V., Prokhorchenko, A., Hrushevska, T., Shcherbyna, R. et. al. (2019). Improvement of efficiency in the organization of transfer trains at developed railway nodes by implementing a "flexible model." Eastern-European Journal of Enterprise Technologies, 2 (3 (98)), 32–39. doi: https://doi.org/10.15587/1729-4061.2019.162143
  • Shramenko, V., Muzylyov, D., Shramenko, N. (2020). Methodology of costs assessment for customer transportation service of small perishable cargoes. International Journal of Business Performance Management, 21 (1/2), 132. doi: https://doi.org/10.1504/ijbpm.2020.10027632
  • De Bok, M., de Jong, G., Tavasszy, L., van Meijeren, J., Davydenko, I., Benjamins, M. et. al. (2018). A multimodal transport chain choice model for container transport. Transportation Research Procedia, 31, 99–107. doi: https://doi.org/10.1016/j.trpro.2018.09.049
  • Karimi, B., Bashiri, M. (2018). Designing a Multi-commodity multimodal splittable supply chain network by logistic hubs for intelligent manufacturing. Procedia Manufacturing, 17, 1058–1064. doi: https://doi.org/10.1016/j.promfg.2018.10.080
  • Zhang, X., Zhang, W., Lee, P. T.-W. (2020). Importance rankings of nodes in the China Railway Express network under the Belt and Road Initiative. Transportation Research Part A: Policy and Practice, 139, 134–147. doi: https://doi.org/10.1016/j.tra.2020.07.003
  • Shramenko, V., Muzylyov, D., Shramenko, N. (2020). Integrated business-criterion to choose a rational supply chain for perishable agricultural goods at automobile transportations. International Journal of Business Performance Management, 21 (1/2), 166. doi: https://doi.org/10.1504/ijbpm.2020.10027634
  • Shramenko, N. Y., Shramenko, V. O. (2019). Optimization of technological specifications and methodology of estimating the efficiency of the bulk cargoes delivery process. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 3, 146–151. doi: https://doi.org/10.29202/nvngu/2019-3/15