A fuzzy model for determining the justifiability of investing in a road freight vehicle fleet

  • Gordan Stojić Faculty of Technical Sciences, University of Novi Sad, Serbia
  • Siniša Sremac Faculty of Technical Sciences, University of Novi Sad, Serbia
  • Igor Vasiljković Faculty of Technical Sciences, University of Novi Sad, Serbia
Keywords: fuzzy logic, road freight transport, vehicle fleet, fleet sizing, investments


A road freight vehicle fleet represents the basic means for the work of a transport company, for which reason it is the most important element of its business doing. Its work directly influences the volume of the income from and costs of business operations of a transport company. The correct sizing and management of the road freight vehicle fleet is of essential significance to the cost-effectiveness of the enterprise and the satisfaction of demands for transporting. The defining of a road freight vehicle fleet and the selection of the vehicles that it will consist of are a complex problem, which should be approached from several aspects.     

In the paper, a fuzzy model for determining the justifiability of investing in the renewal of a truck road freight vehicle fleet and the assessment of the time period needed for return on such investment is presented. The forecasts of the expected volume of transport, i.e. income from transport, have been made on the routes with constant flows of freight for the realistic pessimistic and optimistic variants for the recommended period of the exploitation of a vehicle.


Download data is not yet available.


Bojovic, N. (2002). A general theory approach to rail freight car fleet sizing, European Journal of Operational Research, Vol. 136, Issue 1, 136–172.
Bojovic, N., Boskovic, B., Milenkovic, M. & Sunjic A. (2010). A two-level approach the problem of rail freight car fleet composition. Transport, Vol. 25, Issue 2, 186-192.
Costa-Salas, Y., Sarache, W. & Überwimmer, M. (2017). Fleet size optimization in the discarded tire collection process. Research in Transportation Business & Management, 24, 81–89.
Choia, E. & Tcha D. W. (2007). A column generation approach to the hetereogeneous fleet vehicle routing problem. Computers & Operations Research, Vol. 34, Issue 7, 2080–2095.
Dantzig, G. B. & Fulkerson, D. R. (1954). Minimizing the number of tankers to meet a fixed schedule. Naval Research Logistics (NRL), Vol. 1, Issue 3, 217–222 (Version of Record online: 24 JUL 2006).
Etezadi, Т. T. & Beasley, J. E. (1983). Vehicle Fleet Composition. The Journal of the Operational Research Society, Vol. 34, No. 1, 87-91.
Kirby, D. (1959). Is Your Fleet the Right Size? Journal of the Operational Research Society. Vol. 10, Issue 4, 252–252.
Lima, C. M. R. R., Goldbarg, M. C. & Goldbarg E. F. G. (2004). A Memetic Algorithm for the Heterogeneus Fleet Vehicle Routing Problem. Electronic Notes in Discrete Mathematics, 18, 171-176.
Loxton, R., Lin, Q. & Teo K. L. (2012). A stochastic fleet composition problem. Computers & Operations Research, Vol. 39, Issue 12, 3177–3184.
Milenković, M. & Bojović N. (2013). A fuzzy random model for rail freight car fleet sizing problem. Transportation Research Part C, 33, 107–133.
Pedrycz, W. & Gomide, F. (2007). Fuzzy Systems Engineering: Toward Human-Centric Computing, Wiley-IEEE Press, (Chapter 10).
Sayarshad H. R., Javadian, N., Tavakkoli-Moghaddam R. & Forghani N. (2010). Solving multi-objective optimization formulation for fleet planning in a railway industry. Annals of Operations Research, Vol. 181, Issue 1, 185–197.
Sawik, B., Faulin, J. & Pérez-Bernabe, E. (2018). Multi-Criteria Optimization for Fleet Size with Environmental Aspects. 20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary. Transportation Research Procedia, 27, 61–68.
Telleza, O., Vercraenea, S., Lehuédéb, F., Pétonb, O. & Monteiroa, T. (2018). The fleet size and mix dial-a-ride problem with reconfigurable vehicle capacity. Transportation Research Part C, 91, 99–12.
Teodorović D. (2008). Swarm intelligence systems for transportation engineering: Principles and applications. Transportation Research Part C: Emerging Technologies, Vol. 16, Issue 6, 651–667.
Valmikia, P., Reddy, S., Panchakarla, G., Kumara, K. Purohit, R. & Suhane, A. (2018). A Study on Simulation Methods for AGV Fleet Size Estimation in a Flexible Manufacturing System, 7th International Conference on Materials Processing and Characterization - ICMPC 2017, 17 - 19 March, 2017, Hyderabad, India, Proceedings 5, 3994–3999.
Wu, P., Hartman, J. C. & Wilson G. R. (2005). An Integreated Model and Solution Approach for Fleet Sizin with Heterogeneous Assets. Transportation Science, Vol. 39, Issue 1, 87 – 103.
How to Cite
Stojić, G., Sremac, S., & Vasiljković, I. (2018). A fuzzy model for determining the justifiability of investing in a road freight vehicle fleet. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 62-75. https://doi.org/10.31181/oresta19012010162s