Simulation of cargo delivery by road carrier: case study of the transportation company
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- Category: Content №2 2022
- Last Updated on 30 April 2022
- Published on 30 November -0001
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Authors:
M.Oliskevych, orcid.org/0000-0001-6237-0785, Lviv National University of Nature Management, Dubliany, Lviv Region, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
I.Taran, orcid.org/0000-0002-3679-2519, Dnipro University of Technology, Dnipro, Ukraine, -mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
T.Volkova, orcid.org/0000-0001-8546-4119, Kharkiv National Automobile and Highway University, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
I.Klymenko, orcid.org/0000-0002-6263-0951, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (2): 118 - 123
https://doi.org/10.33271/nvngu/2022-2/118
Abstract:
Purpose. To develop a method of simulation of the process of execution of random orders, which would allow substantiating a set of decisions of the transport company Trans-Service Ltd. The decisions concern the use of their own rolling stock, or the involvement of leased vehicles, as well as the rational sequence of orders.
Methodology. A simulation model of transport cycles with discrete time is developed. The smallest indivisible duration of a cycle is one working shift. The incoming flow of orders is reflected by the random coordinates of the point of departure and destination of goods. The coordinates of potential orders are formed by a random number generator. Each order is set with its characteristics, which include: point of departure and delivery point, delivery volume, average delivery time, group size, time window. At each step of route planning, a set of orders is known, which are characterized by their compatibility. Rules for selecting orders and distributing them among existing vehicles have been developed. An algorithm and a computer program for simulation have been developed.
Findings. Simulation was performed for 30 calendar days, when incoming order flows are stationary. The number of simulation steps is appropriate. The simulation was performed with 20 repetitions. The results are presented by the average value of repetitions. The dependences of the number of orders received, executed, and rejected by the carrier, as well as the number of their own vehicles used by the enterprise are obtained. We also received the number of orders that are not fulfilled by Companys own transport, but are accepted for execution with the help of leased fleet. The allowable order compatibility ratio varied for each series of experiments. The corresponding time indicators of cooperation under conditions of different intensity of the input flow were obtained. To perform simulation experiments with the initial data, which were observed in the transport company Trans-Service Ltd, Ukraine, an array of initial data was formed.
Originality. For the first time, an indicator of organizational and technological compatibility of orders was used to select orders to be serviced by the transport company during simulation, which made it possible to select orders from the stochastic flow and form a rational sequence of their execution.
Practical value. The obtained results are useful in developing a freight plan based on the data obtained on freight orders and the status and capabilities of partners.
Keywords: freight transportation, stochastic process, simulation modeling, order compatibility
References.
1. Sabraliev, N., Abzhapbarova, A., Nugymanova, G., Taran, I., & Zhanbirov, Zh. (2019). Modern aspects of modeling of transport routes in Kazakhstan. News of the National Academy of sciences of the Republic Kazahstan, 2(434), 62-68. https://doi.org/10.32014/2019.2518-170X.39.
2. Nugymanova, G., Nurgaliyeva, M., Zhanbirov, Zh., Naumov, V., & Taran, I. (2021). Choosing a servicing companys strategy while interacting with freight owners at the road transport market. Naukovyi visnyk Natsionalnoho Hirnychoho Universytetu, (1), 204-210. https://doi.org/10.33271/nvngu/2021-1/204.
3. Naumov, V. (2012). Definition of the optimal strategies of transportation market participators. Transport Problems, 7(1), 43-52. Retrieved from http://transportproblems.polsl.pl/pl/Archiwum/2012/zeszyt1/2012t7z1_05.pdf.
4. Naumov, V., Taran, I., Litvinova, Y., & Bauer, M. (2020). Optimizing Resources of Multimodal Transport Terminal for Material Flow Service. Sustainability, 12(16), 6545. https://doi.org/10.3390/su12166545.
5. Gansterer, M., Hartl, R.F., & Vetschera, R. (2019). The cost of incentive compatibility in auction-based mechanisms for carrier collaboration. Networks, 73(4), 490-514. https://doi.org/10.1002/net.21828.
6. Apfelstdt, A., Dashkovskiy, S., & Nieberding, B. (2016). Modeling, optimization and solving strategies for matching problems in cooperative full truckload networks. IFAC-PapersOnLine, 49(2), 18-23. https://doi.org/10.1016/j.ifacol.2016.03.004.
7. Pasha, J., Dulebenets, M.A., Kavoosi, M., Abioye, O.F., Wang,H., & Guo, W. (2020). An optimization model and solution algorithms for the vehicle routing problem with a factory-in-a-box. IEEE Access, 8, 134743-134763. https://doi.org/10.1109/ACCESS.2020.3010176.
8. Mehrdad, R., Kamal, B., & Saman, F. (2021). A novel community detection based genetic algorithm for feature selection. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00483-1.
9. Wang, D., Zhu, J., Wei, X., Cheng, T.C.E., Yin, Y., & Wang, Y. (2019). Integrated production and multiple trips vehicle routing with time windows and uncertain travel times. Computers & Operations Research, 103, 1-12. https://doi.org/10.1016/j.cor.2018.10.011.
10. Qiu, M., Fu, Z., Eglese, R., & Tang, Q. (2018). A Tabu Search algorithm for the vehicle routing problem with discrete split deliveries and pickups. Computers & Operations Research, 100, 102-116. https://doi.org/10.1016/j.cor.2018.07.021.
11. Liu, Y. (2019). An optimization-driven dynamic vehicle routing algorithm for on-demand meal delivery using drones. Computers & Operations Research, 111, 1-20. https://doi.org/10.1016/j.cor.2019.05.024.
12. Chen, L., Zhao, X., Tang, O., Price, L., Zhang, S., & Zhu, W. (2017). Supply chain collaboration for sustainability: A literature review and future research agenda. International Journal of Production Economics, 194, 73-87. https://doi.org/10.1016/j.ijpe.2017.04.005.
13. Colicchia, C., Creazza, A., No, C., & Strozzi, F. (2019). Information sharing in supply chains: a review of risks and opportunities using the systematic literature network analysis (SLNA). Supply chain management: an international journal, 24(1). https://doi.org/10.1108/SCM-01-2018-0003.
14. Hezarkhani, B., Slikker, M., & Van Woensel, T. (2016). A competitive solution for cooperative truckload delivery. OR Spectrum, 38(1), 51-80. https://doi.org/10.1007/s00291-015-0394-y.
15. Gorbachev, P.F., & Mospan, N.V. (2017). Simulation model of service of one-time orders for long-distance cargo transportation. Bulletin of KhNADU, 76, 32-39. Retrieved from https://dspace.khadi.kharkov.ua/dspace/bitstream/123456789/2033/1/V_06_76.pdf.
16. Bychkov, I., Oparin, G., Tchernykh, A., Feoktistov, A., Bogdanova, V., Dyadkin, Y., & Basharina, O. (2017). Simulation modeling in heterogeneous distributed computing environments to support decisions making in warehouse logistics. Procedia engineering, 201, 524-533. https://doi.org/10.1016/j.proeng.2017.09.647.
17. Dzinko, A.M., & Yampolsky, L.S. (2017). Modeling of the material flow scheduling function based on discrete-stochastic dynamic programming. Interdepartmental scientific and technical collection. Adaptive automatic control systems, 2(29), 52-59. https://doi.org/10.20535/1560-8956.30.2017.117703.
18. Sahin, C., Demirtas, M., Erol, R., Baykasolu, A., & Kaplanolu,V. (2017). A multi-agent based approach to dynamic scheduling with flexible processing capabilities. Journal of Intelligent Manufacturing,28(8), 1827-1845. https://doi.org/10.1007/s10845-015-1069-x.
19. Consonni, C., Sottovia, P., Montresor, A., & Velegrakis, Y. (2019). Discovering order dependencies through order compatibility. In Advances in Database Technology-EDBT 2019, 409-420. https://doi.org/10.5441/002/edbt.2019.36.
20. Sharai, S., Oliskevych, M., & Roi, M. (2019). Development of the Procedure for Simulation Modeling of Interrelated Transport Processes on the Main Road Network. Eastern-European Journal of Enterprise Technologies, 5(3), 70-83. https://doi.org/10.15587/1729-4061.2019.179042.
21. Azi, N., Gendreau, M., & Potvin, J.Y. (2012). A dynamic vehicle routing problem with multiple delivery routes. Annals of Operations Research, 199(1), 103-112. https://doi.org/10.1007/s10479-011-0991-3.
22. Roy, M. (2020). Method of optimization of integrated transport process of freight road transport. Scientific notes of TNU named after V.I.Vernadsky. Series: technical sciences, 31(70(5)), 220-227. https://doi.org/10.32838/2663-5941/2020.5/36.
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