Planning vehicle routes to optimize fuel consumption
Abstract
Introduction/purpose: Models developed for routing transport vehicles with an environmental focus are predominantly dedicated to reverse logistics or transporting environmentally hazardous cargo. Few models in the relevant literature consider the ecological factors for routing vehicles involved in the distribution of consumer goods.
Methods: This paper presents a model for planning vehicle routes to optimize fuel consumption, considering the time windows required for service and payload capacity of vehicles. A heuristic algorithm was developed to minimize fuel consumption. A Simulated Annealing metaheuristic was applied to enhance the solutions obtained by the proposed heuristic.
Results: The results from the heuristic algorithm for fuel consumption minimization and the improved results using the Simulated Annealing metaheuristic are presented. All tests were conducted on Solomon's instances.
Conclusion: The developed approach to vehicle routing ensures a compromise between transport companies and ecology. The results show that applying this approach can simultaneously minimize the costs of the transport company and CO2 emissions.
References
Asghari, M. & Mirzapour Al-E-Hashem, S.M.J. 2021. Green vehicle routing problem: A state-of-the-art review. International Journal of Production Economics, 231, art.number:107899. Available at: https://doi.org/10.1016/j.ijpe.2020.107899.
Braekers, K., Ramaekers, K. & Van Nieuwenhuyse, I. 2016. The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99, pp.300-313. Available at: https://doi.org/10.1016/j.cie.2015.12.007.
Chen, C., Demir, E. & Huang, Y. 2021. An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and delivery robots. European Journal of Operational Research, 294(3), pp.1164-1180. Available at: https://doi.org/10.1016/j.ejor.2021.02.027.
Ćirković, M. 2018. Ovo niste znali! Kolika je štetnost pogonskih goriva vozila. Ekovest, April 19 [online] Available at: www.eko-vest.com/ovo-niste-znali-kolika-je-stetnost-pogonskih-goriva-vozila/ (in Serbian) [Accessed: 10 January 2025].
Dantzig, G.B. & Ramser, J.H. 1959. The Truck Dispatching Problem. Management science, 6(1), pp.80-91. Available at: https://doi.org/10.1287/mnsc.6.1.80.
Desrochers, M., Lenstra, J., Savelsbergh, M.W.P., Soumis, F. 1988. Vehicle Routing with Time Windows: Optimization and Approximation. In: Golden, B.L. & Assad, A.A. (Eds.) Vehicle Routing: Methods and Studies, pp.65-84. Elsevier Science Publishers B.V. (North-Holland) [online]. Available at: https://ir.cwi.nl/pub/2036/2036D.pdf [Accessed: 10 January 2025].
Herdianti, W., Santoso Gunawan, A.A. & Komsiyah, S. 2021. Distribution cost optimization using pigeon inspired optimization method with reverse learning mechanism. Procedia Computer Science, 179, pp.920-929. Available at: https://doi.org/10.1016/j.procs.2021.01.081.
Huang, S.-H., Huang, Y.-H., Blazquez, C.A. & Chen, C.-Y. 2022. Solving the vehicle routing problem with drone for delivery services using an ant colony optimization algorithm. Advanced Engineering Informatics, 51, art.number:101536. Available at: https://doi.org/10.1016/j.aei.2022.101536.
Kirkpatrick, S., Gelatt Jr, C.D. & Vecchi, M.P. 1983. Optimization by Simulated Annealing. Science, 220(4598), pp.671-680. Available at: https://doi.org/10.1126/science.220.4598.671.
Konstantakopoulos, G.D., Gayialis, S.P. & Kechagias, E.P. 2022. Vehicle routing problem and related algorithms for logistics distribution: A literature review and classification. Operational Research, 22(3), pp.2033-2062. Available at: https://doi.org/10.1007/s12351-020-00600-7.
Liao, W., Liu, L. & Fu, J. 2019. A Comparative Study on the Routing Problem of Electric and Fuel Vehicles Considering Carbon Trading. International Journal of Environmental Research and Public Health, 16(17), art.number:3120. Available at: https://doi.org/10.3390/ijerph16173120.
Liu, C., Kou, G., Zhou, X., Peng, Y., Sheng, H. & Alsaadi, F.E. 2020. Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach. Knowledge-Based Systems, 188, art.number:104813. Available at: https://doi.org/10.1016/j.knosys.2019.06.021.
Marinakis, Y. & Migdalas, A. 2007. Annotated bibliography in vehicle routing. Operational Research, 7, pp.27-46. Available at: https://doi.org/10.1007/BF02941184.
Napoli, G., Micari, S., Dispenza, G., Andaloro, L., Antonucci, V. & Polimeni, A. 2021. Freight distribution with electric vehicles: A case study in Sicily. RES, infrastructures and vehicle routing. Transportation Engineering, 3, art.number:100047. Available at: https://doi.org/10.1016/j.treng.2021.100047.
Pan, B., Zhang, Z. & Lim, A. 2021. Multi-trip time-dependent vehicle routing problem with time windows. European Journal of Operational Research, 291(1), pp.218-231. Available at:. https://doi.org/10.1016/j.ejor.2020.09.022.
Ramadhani, B.N.I.F. & Garside, A.K. 2021. Particle swarm optimization algorithm to solve vehicle routing problem with fuel consumption minimization. JOSI Jurnal Optimasi Sistem Industri, 20(1), pp.1-10 [online]. Available at: https://josi.ft.unand.ac.id/index.php/josi/article/view/140 [Accessed: 10 January 2025].
-Sintef. 2008. Solomon bechmark. Sintef, April 18 [online]. Available at: https://www.sintef.no/projectweb/top/vrptw/solomon-benchmark/ [Accessed: 10 January 2025].
Song, M.-x., Li, J.-q., Han, Y.-q., Han, Y.-y., Liu, L.-l. & Sun, Q. 2020. Metaheuristics for solving the vehicle routing problem with the time windows and energy consumption in cold chain logistics. Applied Soft Computing, 95, art.number:106561. Available at: https://doi.org/10.1016/j.asoc.2020.106561.
Teodorović, D. 2007. Transportne mreže, 4th Edition. Belgrade: University of Belgrade, Faculty of Transport and Traffic Engineering (in Serbian). ISBN: 978-86-7395-239-0.
Teodorović, D. & Pavković, G. 1992. A simulated annealing technique approach to the vehicle routing problem in the case of stochastic demand. Transportation Planning and Technology, 16(4), pp.261-273. Available at: https://doi.org/10.1080/03081069208717490.
Tiwari, A. & Chang, P. 2015. A block recombination approach to solve green vehicle routing problem. International Journal of Production Economics, 164, pp.379-387. Available at: https://doi.org/10.1016/j.ijpe.2014.11.003.
Xiao, Y., Zhao, Q., Kaku, I. & Xu, Y. 2012. Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 39(7), pp.1419-1431. Available at: https://doi.org/10.1016/j.cor.2011.08.013.
Copyright (c) 2025 Predrag Grozdanović, Miloš Nikolić, Milica Šelmić
This work is licensed under a Creative Commons Attribution 4.0 International License.
Proposed Creative Commons Copyright Notices
Proposed Policy for Military Technical Courier (Journals That Offer Open Access)
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).