INFORMATION TECHNOLOGY OF DECISION SUPPORT TO DESIGN THE TRANSPORTATION ORDERS' SERVICING
Keywords:rational number, utilization rate, experiment, simulation modeling, regression analysis.
The main purpose of most transport companies is to provide the quality services to customers with minimal costs. At the same time, determination of the number of vehicles and their utilization rate when satisfying transportation orders is the important task, the proper solution of which leads to the full and timely servicing and contributes to an increase of a transport company's competitiveness in the present-day market. Due to the analysis results of the state-of-the-art literature and Internet sources, it has been revealed that the problem of finding the rational fleet size and the rate of its utilization to complete the transportation orders were not fully solved. From the criteria analysis it has been proposed to substantiate the vehicle fleet size according to the car utilization rate to be assigned as the "vehicle working time-to-total working time" ratio. Considering the probabilistic nature of the transportation process, a simulation model to complete the orders by a truck fleet has been developed in the AnyLogic environment. An experimental plan has been developed to reproduce the real transportation order conditions and consisted of 27 series, each of which was of 100 experiments. The variation range of input factors, which was the transportation distance, vehicles' number and orders' hourly intensity were [10;30], [1;3] and [0.6;1], respectively. From the experimental results processing by the regression analysis methods, it has been found that the dependence of changes in the car utilization rate, transportation distance, vehicle' number and orders' intensity was of linear form. The obtained dependence has been estimated via the determination coefficient, which was 0.95, and indicated the high quality of the model proposed. The resulting model allows calculating the required number of vehicles from their operating conditions. In the case study the 2 vehicles were recommended to service the transportation orders. Further research efforts can be taking into account a larger number of influencing factors, increasing their variation range and obtaining dependencies to describe the presented criterion change to acceptable accuracy.
2. Hoff, A., Andersson, H., Christiansen, M., Hasle G., & Løkketangen, A. (2010). Industrial aspects and literature survey: Fleet composition and routing. Computers & Operations Research, 37 (12), 2041–2061.
3. Milenković, M., & Bojović, N. (2013). A fuzzy random model for rail freight car fleet sizing problem Transportation Research Part C: Emerging Technologies, 33, 107-133.
4. Sayarshad, H. R., & Tavakkoli-Moghaddam, R. (2010). Solving a multi periodic stochastic model of the rail-car fleet sizing by two-stage formulation. Applied Mathematical Modelling, 34(5), 1164–1174.
5. Yaghini, M., & Khandaghabadi, Z. (2013). A hybrid metaheuristic algorithm for dynamic rail car fleet sizing problem. Applied Mathematical Modelling, 37 (6), 4127–4138.
6. Sayarshad, H.R., & Ghoseiri, K. (2009). A simulated annealing approach for the multi-periodic rail-car fleet sizing problem. Computers & Operations Research, 36 (6), 1789–1799.
7. Milenković, Miloš S., Bojović, Nebojša J., Švadlenka, Libor, & Melichar, Vlastimil (2015). A stochastic model predictive control to heterogeneous rail freight car fleet sizing problem. Transportation Research Part E: Logistics and Transportation Review, Elsevier, 82(C), 162–198.
8. Barrios, J.A., & Godier, J.D. (2014). Fleet Sizing for Flexible Carsharing Systems: Simulation-Based Approach. Transportation Research Record, 2416(1), 1-9. doi:10.3141/2416-01.
9. Redmer, A. (2015). Strategic vehicle fleet management – the composition problem. LogForum 11 (1), 119–126.
10. Zak, Jacek. (2008). Multiple objective optimization of the fleet sizing problem for road freight transportation. Journal of Advanced Transportation, 42, (4), 379–427.
11. Naumov, V.S. (2006). Formation of a vehicle fleet rational structure in conditions of random characteristics of the transportation orders' flow. Candidate thesis. Kharkiv.
12. Anylogic Simulation Software. (2019). Retrieved from: https://www.anylogic.com.
13. Bauer Vladimir, Bazanov, Artem, V., Kozin, Evgeniy, S., Nemkov, Vasiliy, M., & Mukhortov, Aleksandr, A. (2019). Optimization Of Technological Transport Sets Using Anylogic Simulation Environment. Journal of Mechanical Engineering Research & Developments, 42(2), 41–43.
14. Lipenkov, A.V. (2015). Improving the efficiency of the urban passenger transportation functioning from managing the stop points' throughput. Candidate thesis. Nizhny Novgorod.
15. Volodarets, M.V. (2018). Features of the AnyLogic application for solving problems of transportation simulation. Materials of the international scientific and methodical Internet conference "Problems of mathematical education: challenges of the present (2018)", Vinnytsia, Ukraine, 280-283.
16. Muravev, Dmitri, Hu, Hao, Rakhmangulov, Aleksandr, Mishkurov, Pavel. (2021). Multi-agent optimization of the intermodal terminal main parameters by using AnyLogic simulation platform: Case study on the Ningbo-Zhoushan Port. International Journal of Information Management, (57), 102–133.
17. Coman, M., & Badea, D. (2017). The Vehicles Traffic Flow Optimization in an Urban Transportation System by Using Simulation Modeling. Land Forces Academy Review, (22), 190–197.
18. Zhang, Y., Wang, Y., Wu, L. (2012). Research on demand-driven leagile supply chain operation model: a simulation based on AnyLogic in system engineering. Syst. Eng. Procedia 3, 249–258.
19. Bannikov, D., Sirina, N. (2018). Model of passenger rolling stock maintenance. MATEC Web of Conferences 216, 02018 Polytransport Systems-2018. Retrieved from: https://doi.org/10.1051/matecconf/201821602018
20. Process Simulation Library Blocks. Retrieved from: https://help.anylogic.ru/index.jsp?topic=%2Fcom.anylogic.help%2Fhtml%2Fprocessmodeling%2Fpml.html
How to Cite
The authors who publish in this collection agree with the following terms:
• The authors reserve the right to authorship of their work and give the magazine the right to first publish this work under the terms of license CC BY-NC-ND 4.0 (with the Designation of Authorship - Non-Commercial - Without Derivatives 4.0 International), which allows others to freely distribute the published work with a mandatory reference to the authors of the original work and the first publication of the work in this magazine.
• Authors have the right to make independent extra-exclusive work agreements in the form in which they were published by this magazine (for example, posting work in an electronic repository of an institution or publishing as part of a monograph), provided that the link to the first publication of the work in this journal is maintained. .
• Journal policy allows and encourages the publication of manuscripts on the Internet (for example, in institutions' repositories or on personal websites), both before the publication of this manuscript and during its editorial work, as it contributes to the emergence of productive scientific discussion and positively affects the efficiency and dynamics of the citation of the published work (see The Effect of Open Access).