Abstract
City logistics and last-mile distribution have gained the interest of practitioners and logistics companies, as well as of consumers and inhabitants. In the case of companies, the objective is to minimize distribution costs and improve the quality of services provided. On the other hand, consumers require fast deliveries and accuracy in time restrictions. Moreover, society and governments are interested in the minimization of greenhouse gas emissions, which greatly affect cities. Addressing effectively the distribution of products in urban areas presupposes the use of advanced algorithms solving optimally the routing of vehicles and scheduling of deliveries, as well as the use of new technology vehicles with minimal fuel consumption and gas emissions. On this premise, the paper proposes a genetic algorithm that addresses the Vehicle Routing Problem with Time Windows and Simultaneous Pickups and Deliveries, while considering the type, characteristics and specifications of the vehicles used. This algorithm aims to minimize the distribution cost while also estimate CO2 emissions, and consequently how these attributes are affected by petrol, diesel, and electric vehicles in the logistics sector. Therefore, the algorithm is tested and evaluated in real-life distribution cases addressed by a logistics company in Greece. The results obtained from the algorithm are compared and evaluated, while proposals for improving the efficiency of deliveries, as well as for reducing greenhouse gas emissions and costs are made.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Braekers, K., Ramaekers, K., & Van Nieuwenhuyse, I. (2016). The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99, 300–313. https://doi.org/10.1016/j.cie.2015.12.007
Chaabane, A., Ramudhin, A., & Paquet, M. (2012). Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135(1), 37–49. https://doi.org/10.1016/j.ijpe.2010.10.025
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91. https://doi.org/10.1287/mnsc.6.1.80
Elshaer, R., & Awad, H. (2020). A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Computers & Industrial Engineering, 140, 106242. https://doi.org/10.1016/j.cie.2019.106242
Erdelić, T., & Carić, T. (2019). A survey on the electric vehicle routing problem: Variants and solution approaches. Journal of Advanced Transportation, 2019, 5075671. https://doi.org/10.1155/2019/5075671
Erdelić, T., Carić, T., Erdelić, M., & Tišljarić, L. (2019). Electric vehicle routing problem with single or multiple recharges. Transportation Research Procedia, 40, 217–224. https://doi.org/10.1016/j.trpro.2019.07.033
European Automobile Manufacturers Association. (2019). Vehicles in use—Europe 2019. https://www.acea.be/publications/article/report-vehicles-in-use-europe-2019
Gayialis, S. P., Konstantakopoulos, G. D., & Tatsiopoulos, I. P. (2019). Vehicle routing problem for urban freight transportation: A review of the recent literature. In A. Sifaleras & K. Petridis (Eds.), Operational research in the digital era—ICT challenges (pp. 89–104). Springer. https://doi.org/10.1007/978-3-319-95666-4_7
Hiermann, G., Puchinger, J., Ropke, S., & Hartl, R. F. (2016). The electric fleet size and mix vehicle routing problem with time windows and recharging stations. European Journal of Operational Research, 252(3), 995–1018. https://doi.org/10.1016/j.ejor.2016.01.038
Hornstra, R. P., Silva, A., Roodbergen, K. J., & Coelho, L. C. (2020). The vehicle routing problem with simultaneous pickup and delivery and handling costs. Computers & Operations Research, 115, 104858. https://doi.org/10.1016/j.cor.2019.104858
Kechagias, E. P., Gayialis, S. P., Konstantakopoulos, G. D., & Papadopoulos, G. A. (2020). An application of an urban freight transportation system for reduced environmental emissions. Systems, 8(4), 49. https://doi.org/10.3390/systems8040049
Konstantakopoulos, G. D., Gayialis, S. P., & Kechagias, E. P. (2020). Vehicle routing problem and related algorithms for logistics distribution: A literature review and classification. Operational Research International Journal, 22(3), 2033–2062. https://doi.org/10.1007/s12351-020-00600-7
Konstantakopoulos, G. D., Gayialis, S. P., Kechagias, E. P., Papadopoulos, G. A., & Tatsiopoulos, I. P. (2021). Delivering and picking goods under time window restrictions: An effective evolutionary algorithm. Journal of Intelligent & Fuzzy Systems, Preprint, 40(3), 5323–5336. https://doi.org/10.3233/JIFS-202129
Li, H., & Lim, A. (2003). Local search with annealing-like restarts to solve the VRPTW. European Journal of Operational Research, 150(1), 115–127. https://doi.org/10.1016/S0377-2217(02)00486-1
Lin, J., Zhou, W., & Wolfson, O. (2016). Electric vehicle routing problem. Transportation Research Procedia, 12, 508–521. https://doi.org/10.1016/j.trpro.2016.02.007
Micheli, G. J. L., Cagno, E., Mustillo, G., & Trianni, A. (2020). Green supply chain management drivers, practices and performance: A comprehensive study on the moderators. Journal of Cleaner Production, 259, 121024. https://doi.org/10.1016/j.jclepro.2020.121024
Ombuki, B., Ross, B. J., & Hanshar, F. (2006). Multi-objective genetic algorithms for vehicle routing problem with time windows. Applied Intelligence, 24(1), 17–30. https://doi.org/10.1007/s10489-006-6926-z
Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2), 254–265. https://doi.org/10.1287/opre.35.2.254
Tan, K. C., Chew, Y. H., & Lee, L. H. (2006). A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem. Computational Optimization and Applications, 34(1), 115–151. https://doi.org/10.1007/s10589-005-3070-3
Tarantilis, C. D., Diakoulaki, D., & Kiranoudis, C. T. (2004). Combination of geographical information system and efficient routing algorithms for real life distribution operations. European Journal of Operational Research, 152(2), 437–453. https://doi.org/10.1016/S0377-2217(03)00035-3
Wassan, N. A., & Nagy, G. (2014). Vehicle routing problem with deliveries and pickups: Modelling issues and meta-heuristics solution approaches. International Journal of Transportation, 2(1), 95–110. https://doi.org/10.14257/ijt.2014.2.1.06
Yu, B., Yang, Z. Z., & Yao, B. Z. (2011). A hybrid algorithm for vehicle routing problem with time windows. Expert Systems with Applications, 38(1), 435–441. https://doi.org/10.1016/j.eswa.2010.06.082
Zhen, L., Huang, L., & Wang, W. (2019). Green and sustainable closed-loop supply chain network design under uncertainty. Journal of Cleaner Production, 227, 1195–1209. https://doi.org/10.1016/j.jclepro.2019.04.098
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Konstantakopoulos, G.D., Kechagias, E.P., Gayialis, S.P., Tatsiopoulos, I.P. (2023). Green Freight Distribution: A Case Study in Greece. In: Matsatsinis, N.F., Kitsios, F.C., Madas, M.A., Kamariotou, M.I. (eds) Operational Research in the Era of Digital Transformation and Business Analytics. BALCOR 2020. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-24294-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-24294-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24293-9
Online ISBN: 978-3-031-24294-6
eBook Packages: Business and ManagementBusiness and Management (R0)