Structural optimization of multimodal routes for cargo delivery

Authors

DOI:

https://doi.org/10.5604/01.3001.0053.7076

Keywords:

multimodal transportation, discrete processes, structural optimization, cargo deliveries

Abstract

This article is devoted to the coordination of single stages of the multimodal delivery process, taking into account the fact that the process is discrete in its content. The tact, which has the content of a time window for performing the operation is used for discrete processes. Due to the fact that multimodal transportation of goods is carried out on a large network, time is one of the most important criteria for their perfection. Two timing criteria are applied in the article, which take into account the fact that the multimodal process must be synchronized and that the transportation of a large group of goods can be carried out in separate parts. An estimation criterion was also applied, which takes into account constant, variable, contingent costs, which are carried out depending on the structure of the process. The goal of the study is to create such multimodal cargo delivery routes that are characterized by the highest level of selection criteria. In contrast to known studies, the dependence of the optimization criteria of the multimodal process on the total volume of cargo delivery was shown. The method of analyzing the transport scheme of multimodal transportation and the corresponding algorithm and computer program were developed. The methodology involves a complete review of all possible route options using three types of continent transport, namely road, rail, and river. The method of structural optimization is applied to the example of a transcontinental transport corridor.

References

Akyüz, M. H., Dekker, R., Azadeh, S. S. (2023). Partial and complete re-planning of an intermodal logistic system under disruptions. Transportation Research Part E: Logistics and Transportation Review, 169, 102968. DOI: 10.1016/j.tre.2022.102968.

Ambrosino, D., Sciomachen, A. (2014). Location of mid-range dry ports in multimodal logistic networks. Procedia-Social and Behavioral Sciences, 108, 118-128. DOI: 10.1016/j.sbspro.2013.12.825.

Ambrosino, D., Sciomachen, A. (2021). Impact of externalities on the design and management of multimodal logistic networks. Sustainability, 13(9), 5080. DOI: 10.3390/su13095080.

Archetti, C., Peirano, L., Speranza, M. G. (2022). Optimization in multimodal freight transportation problems: A survey. European Journal of Operational Research, 299(1), 1-20. DOI: 10.1016/j.ejor.2021.07.031.

Baykasoğlu, A., Subulan, K., Taşan, A. S., Dudaklı, N. (2019). A review of fleet planning problems in single and multimodal transportation systems. Transportmetrica A: Transport Science, 15(2), 631-697. DOI: 10.1080/23249935.2018.1523249.

Beresford, A. K. C., Banomyong, R., Pettit, S. (2021). A Critical Review of a Holistic Model Used for Assessing Multimodal Transport Systems. Logistics 2021, 5, 11. DOI: 10.3390/logistics5010011.

Castelli, L., Pesenti, R., Ukovich, W. (2004). Scheduling multimodal transportation systems. European Journal of Operational Research, 155(3), 603-615. DOI: 10.1016/j.ejor.2003.02.002.

Chen, Q., Chen, H. (2013). Solution algorithm for a new bi-level discrete network design problem. Promet-Traffic&Transportation, 25(6), 513-524. DOI: 10.7307/ptt.v25i6.1424.

Cieśla, M., Opasiak, T. (2021). Mining machines elements packing and securing on platform container. Scientific Journal of Silesian University of Technology. Series Transport., 110, 05-21. DOI: 10.20858/sjsutst.2021.110.1.

Giusti, R., Iorfida, C., Li, Y., Manerba, D., Musso, S., Perboli, G., ... Yuan, S. (2019). Sustainable and de-stressed international supply-chains through the SYNCHRONET approach. Sustainability, 11(4), 1083. DOI: 10.3390/su11041083.

Grznar, P., Gregor, M., Gaso, M., Gabajova, G., Schickerle, M., Burganova, N. (2021). Dynamic simulation tool for planning and optimisation of supply process. International journal of simulation modelling, 20(3), 441-452. DOI: 10.2507/IJSIMM20-3-552.

Fan, Y., Ding, J., Liu, H., Wang, Y., Long, J. (2022). Large-scale multimodal transportation network models and algorithms-Part I: The combined mode split and traffic assignment problem. Transportation Research Part E: Logistics and Transportation Review, 164, 102832. DOI: 10.1016/j.tre.2022.102832.

Fischer, S. (2021). Investigation of effect of water content on railway granular supplementary layers. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 64-68. DOI: 10.33271/nvngu/2021-3/064.

Hochbaum, D. S., Levin, A. (2006). Cyclical scheduling and multi-shift scheduling: Complexity and approximation algorithms. Discrete Optimization, 3(4), 327-340. http://dx.doi.org/10.1016/j.disopt.2006.02.002.

Karimi, B., Bashiri, M. (2018). Designing a Multi-commodity multimodal split-table supply chain network by logistic hubs for intelligent manufacturing. Procedia manufacturing, 17, 1058-1064. DOI: 10.1016/j.promfg.2018.10.080.

Kézai, P.K., Fischer, S., Lados, M. (2020). Smart Economy and Startup Enterprises in the Visegrád Countries – A Comparative Analysis Based on the Crunch base Database. Smart Cities, 3, 1477-1494. DOI: 10.3390/smartcities3040070.

Lada, A. N., Sazonov, V. V., Skobelev, P. O. (2016). Method for transportation cost calculation on the basis of full cycle (round trip). Indian Journal of Science and Technology, 9(20), 1-6. DOI: 10.17485/ijst/2016/v9i20/94478.

Lee, H., Choo, S. (2016). Optimal decision making process of transportation service providers in maritime freight networks. KSCE Journal of Civil Engineering, 20, 922-932. DOI 10.1007/s12205-015-0116-7.

Lunardi, W. T., Birgin, E. G., Laborie, P., Ronconi, D. P., Voos, H. (2020). Mixed integer linear programming and constraint programming models for the online printing shop scheduling problem. Computers & Operations Research, 123, 105020. DOI: 10.1016/j.cor.2020.105020.

Macioszek, E., Cieśla, M., Granà, A. (2023). Future Development of an Energy-Efficient Electric Scooter Sharing System Based on a Stakeholder Analysis Method. Energies, 16, 554. DOI: 10.3390/en16010554.

Monek, G. D., Fischer, S. (2023). IIoT-Supported Manufacturing-Material-Flow Tracking in a DES-Based Digital-Twin Environment. Infrastructures, 8(4), 75. DOI: 10.3390/infra-structures8040075.

Naumov, V., Taran, I., Litvinova, Z., Bauer, M. (2020). Optimizing resources of multimodal transport terminal for material flow service. Sustainability (Switzerland), 12(16), 6545. DOI: 10.3390/su12166545.

Oliskevych, M. (2018). Optimization of periodic unitary online schedule of transport tasks of highway road trains. Transport problems, 13. DOI: 10.21307/tp.+2018.13.1.10.

Oliskevych, M., Taran, I., Volkova, T., Klymenko, I. (2022). Simulation of cargo delivery by road carrier: case study of the transportation company. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (2), 118-123. DOI: 10.33271/nvngu/2022-2/118.

Peng, Y., Luo, Y. J., Jiang, P., Yong, P. C. (2022). The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach. Engineering Computations, 39(2), 587-608. DOI: 10.1051/ro/2020110.

Poland-Ukraine set up ‘Black Sea to Baltic’ intermodal corridor https://www.rail-freight.com/corridors/2020/12/15/poland-ukraine-set-up-black-sea-to-baltic-intermodal-corridor/.

SteadieSeifi, M., Dellaert, N. P., Nuijten, W., Van Woensel, T., Raoufi, R. (2014). Multimodal freight transportation planning: A literature review. European journal of operational research, 233(1), 1-15. DOI: 10.1016/j.ejor.2013.06.055.

Sun, Y. (2022). A Robust Possibilistic Programming Approach for a Road-Rail Intermodal Routing Problem with Multiple Time Windows and Truck Operations Optimization under Carbon Cap-and-Trade Policy and Uncertainty. Systems, 10(5), 156. DOI: 10.3390/systems10050156.

Taran, I., Karsybayeva, A., Naumov, V., Murzabekova, K., Chazhabayeva, M. (2023). Fuzzy-Logic Approach to Estimating the Fleet Efficiency of a Road Transport Company: A Case Study of Agricultural Products Deliveries in Kazakhstan. Sustainability (Switzerland), 15(5): 4179. DOI: 10.3390/su15054179.

Van Riessen, B., Negenborn, R. R., Lodewijks, G., Dekker, R. (2015). Impact and relevance of transit disturbances on planning in intermodal container networks using disturbance cost analysis. Maritime Economics & Logistics, 17, 440-463. DOI: 10.1057/mel.2014.27.

Wang, Y., Liu, H., Fan, Y., Ding, J., Long, J. (2022). Large-scale multimodal transportation network models and algorithms-Part II: Network capacity and network design problem. Transportation Research Part E: Logistics and Transportation Review, 167, 102918. DOI: 10.1016/j.tre.2022.102918.

Wang, S., Fu, S. (2022). Path design and planning and investment and construction mode of multimodal transport network based on big data analysis. Discrete dynamics in nature and soci-ety, 2022. DOI: 10.1155/2022/9185372.

Udomwannakhet, J., Vajarodaya, P., Manicho, S., Kaewfak, K., Ruiz, J. B., Ammarapala, V. (2018, May). A review of multimodal transportation optimization model. In 2018 5th International Conference on Business and Industrial Research (ICBIR) (pp. 333-338). IEEE. DOI: 10.1109/ICBIR.2018.8391217.

Zhang, X., Jin, F. Y., Yuan, X. M., Zhang, H. Y. (2021). Low-Carbon Multimodal Transportation Path Optimization under Dual Uncertainty of Demand and Time. Sustainability, 13(15), 8180. DOI: 10.3390/su13158180.

Zhang, H., Li, Y., Zhang, Q., Chen, D. (2021). Route selection of multimodal transport based on China railway transportation. Journal of Advanced Transportation, 2021, 1-12. DOI: 10.1155/2021/9984659.

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Published

2023-09-30

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Section

Original articles

How to Cite

Taran, I., Olzhabayeva, R., Oliskevych, M., & Danchuk, V. (2023). Structural optimization of multimodal routes for cargo delivery. Archives of Transport, 67(3), 49-70. https://doi.org/10.5604/01.3001.0053.7076

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