Multi-objective electric vehicle cold chain logistics location-routing problem with charging-stations
DOI: https://doi.org/10.3846/transport.2026.22870Abstract
This study focuses on an Electric Vehicle (EV) cold chain logistics location-routing problem with charging-stations that aims at optimizing the location of depots, the delivery routing of EVs, the time planning for visiting all customers and the location of charging-stations. To tackle this problem, a multi-objective optimization model is established to minimize cold chain logistics cost, and at the same time maximize cold chain logistics network efficiency. An integrated algorithm that combines an Improved Artificial Fish Swarm Algorithm (IAFSA) and a Label-based Charging-station Optimization Algorithm (LCOA) is used to solve the proposed model. Extensive computational experiments are conducted to demonstrate the applicability of the proposed model, and show the efficiency of the developed algorithm. Moreover, the effects of battery driving range and traveling speed on the results are explored through the sensitivity analysis that provides decision supports for logistics enterprises to operate an EV cold chain logistics network.
First published online 31 July 2026
Keywords:
cold chain logistics, electric vehicle, location-routing problem, charging-station, multi-objective optimization, artificial fish swarm algorithmHow to Cite
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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References
Azad, M. A. K.; Rocha, A. M. A. C.; Fernandes, E. M. G. P. 2014. Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems, Swarm and Evolutionary Computation 14: 66–75. https://doi.org/10.1016/j.swevo.2013.09.002
Bac, U.; Erdem, M. 2021. Optimization of electric vehicle recharge schedule and routing problem with time windows and partial recharge: a comparative study for an urban logistics fleet, Sustainable Cities and Society 70: 102883. https://doi.org/10.1016/j.scs.2021.102883
Çalık, H.; Oulamara, A.; Prodhon, C.; Salhi, S. 2021. The electric location-routing problem with heterogeneous fleet: formulation and Benders decomposition approach, Computers & Operations Research 131: 105251. https://doi.org/10.1016/j.cor.2021.105251
Eggers, Fe.; Eggers, Fa. 2011. Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model, Technological Forecasting and Social Change 78(1): 51–62. https://doi.org/10.1016/j.techfore.2010.06.014
Ghobadi, A.; Tavakkoli Moghaddam, R.; Fallah, M. 2021. Multi-depot electric vehicle routing problem with fuzzy time windows and pickup/delivery constraints, Journal of Applied Research on Industrial Engineering 8(1): 1–18.
He, Q.; Hu, X.; Ren, H.; Zhang, H. 2015. A novel artificial fish swarm algorithm for solving large-scale reliability–redundancy application problem, ISA Transactions 59: 105–113. https://doi.org/10.1016/j.isatra.2015.09.015
Hof, J.; Schneider, M.; Goeke, D. 2017. Solving the battery swap station location-routing problem with capacitated electric vehicles using an AVNS algorithm for vehicle-routing problems with intermediate stops, Transportation Research Part B: Methodological 97: 102–112. https://doi.org/10.1016/j.trb.2016.11.009
Jabbarzadeh, A.; Azad, N.; Verma, M. 2020. An optimization approach to planning rail hazmat shipments in the presence of random disruptions, Omega 96: 102078. https://doi.org/10.1016/j.omega.2019.06.004
James, S. J.; James, C. 2010. The food cold-chain and climate change, Food Research International 43(7): 1944–1956. https://doi.org/10.1016/j.foodres.2010.02.001
Jie, W.; Yang, J.; Zhang, M.; Huang, Y. 2019. The two-echelon capacitated electric vehicle routing problem with battery swapping stations: formulation and efficient methodology, European Journal of Operational Research 272(3): 879–904. https://doi.org/10.1016/j.ejor.2018.07.002
Jung, J.; Jayakrishnan, R. 2012. High-coverage point-to-point transit: electric vehicle operations, Transportation Research Record: Journal of the Transportation Research Board 2287: 44–53. https://doi.org/10.3141/2287-06
Koç, Ç.; Bektaş, T.; Jabali, O.; Laporte, G. 2016. The fleet size and mix location-routing problem with time windows: formulations and a heuristic algorithm, European Journal of Operational Research 248(1): 33–51. https://doi.org/10.1016/j.ejor.2015.06.082
Leng, L.; Zhang, C.; Zhao, Y.; Wang, W.; Zhang, J.; Li, G. 2020. Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches, Journal of Cleaner Production 273: 122801. https://doi.org/10.1016/j.jclepro.2020.122801
Li, L.; Wang, W.; Xu, X. 2017. Multi-objective particle swarm optimization based on global margin ranking, Information Sciences 375: 30–47. https://doi.org/10.1016/j.ins.2016.08.043
Li, X.; Shao, Z. 2002. An optimizing method based on autonomous animals: fish-swarm algorithm, Systems Engineering – Theory & Practice 22(11): 32–38. https://doi.org/10.3321/j.issn:1000-6788.2002.11.007 (in Chinese).
Lin, C. K. Y.; Kwok, R. C. W. 2006. Multi-objective metaheuristics for a location-routing problem with multiple use of vehicles on real data and simulated data, European Journal of Operational Research 175(3): 1833–1849. https://doi.org/10.1016/j.ejor.2004.10.032
Liu, G.; Hu, J.; Yang, Y.; Xia, S.; Lim, M. K. 2020a. Vehicle routing problem in cold chain logistics: a joint distribution model with carbon trading mechanisms, Resources, Conservation and Recycling 156: 104715. https://doi.org/10.1016/j.resconrec.2020.104715
Liu, Y.; Feng, X., Zhang, L.; Hua, W.; Li, K. 2020b. A Pareto artificial fish swarm algorithm for solving a multi-objective electric transit network design problem, Transportmetrica A: Transport Science 16(3): 1648–1670. https://doi.org/10.1080/23249935.2020.1773574
Luo, J.; Li, X.; Chen, M.-R.; Liu, H. 2015. A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows, Information Sciences 316: 266–292. https://doi.org/10.1016/j.ins.2015.04.001
Ropke, S.; Pisinger, D. 2006. An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows, Transportation Science 40(4): 455–472. https://doi.org/10.1287/trsc.1050.0135
Sassi, O.; Oulamara, A. 2017. Electric vehicle scheduling and optimal charging problem: complexity, exact and heuristic approaches, International Journal of Production Research 55(2): 519–535. https://doi.org/10.1080/00207543.2016.1192695
Sayarshad, H. R.; Mahmoodian, V.; Gao, H. O. 2020. Non-myopic dynamic routing of electric taxis with battery swapping stations, Sustainable Cities and Society 57: 102113. https://doi.org/10.1016/j.scs.2020.102113
Schiffer, M.; Walther, G. 2017. The electric location routing problem with time windows and partial recharging, European Journal of Operational Research 260(3): 995–1013. https://doi.org/10.1016/j.ejor.2017.01.011
Schneider, M.; Stenger, A.; Goeke, D. 2014. The electric vehicle-routing problem with time windows and recharging stations, Transportation Science 48(4): 500–520. https://doi.org/10.1287/trsc.2013.0490
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
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: 106561. https://doi.org/10.1016/j.asoc.2020.106561
Verma, M.; Verter, V.; Zufferey, N. 2012. A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials, Transportation Research Part E: Logistics and Transportation Review 48(1): 132–149. https://doi.org/10.1016/j.tre.2011.06.001
Wang, S.; Tao, F.; Shi, Y.; Wen, H. 2017. Optimization of vehicle routing problem with time windows for cold chain logistics based on carbon tax, Sustainability 9(5): 694. https://doi.org/10.3390/su9050694
Yang, J.; Sun, H. 2015. Battery swap station location-routing problem with capacitated electric vehicles, Computers & Operations Research 55: 217–232. https://doi.org/10.1016/j.cor.2014.07.003
Yang, W.-H. 2014. An improved artificial fish swarm algorithm and its application in multiple sequence alignment, Journal of Computational and Theoretical Nanoscience 11(3): 888–892.
Yu, V. F.; Normasari, N. M. E.; Chen, W.-H. 2021. Location-routing problem with time-dependent demands, Computers & Industrial Engineering 151: 106936. https://doi.org/10.1016/j.cie.2020.106936
Zhang, D. Z.; Eglese, R.; Li, S. 2018. Optimal location and size of logistics parks in a regional logistics network with economies of scale and CO2 emission taxes, Transport 33(1): 52–68. https://doi.org/10.3846/16484142.2015.1004644
Zhang, S.; Chen, N.; Song, X.; Yang, J. 2019a. Optimizing decision-making of regional cold chain logistics system in view of low-carbon economy, Transportation Research Part A: Policy and Practice 130: 844–857. https://doi.org/10.1016/j.tra.2019.10.004
Zhang, S.; Chen, M.; Zhang, W. 2019b. A novel location-routing problem in electric vehicle transportation with stochastic demands, Journal of Cleaner Production 221: 567–581. https://doi.org/10.1016/j.jclepro.2019.02.167
Zhang, L.-Y.; Tseng, M.-L.; Wang, C.-H.; Xiao, C.; Fei, T. 2019c. Low-carbon cold chain logistics using ribonucleic acid-ant colony optimization algorithm, Journal of Cleaner Production 233: 169–180. https://doi.org/10.1016/j.jclepro.2019.05.306
Zhang, S.; Chen, M.; Zhang, W.; Zhuang, X. 2020. Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations, Expert Systems with Applications 145: 113123. https://doi.org/10.1016/j.eswa.2019.113123
Zhang, Z.; Wang, K.; Zhu, L.; Wang, Y. 2017. A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem, Expert Systems with Applications 86: 165–176. https://doi.org/10.1016/j.eswa.2017.05.053
Zhao, Z.; Li, X.; Zhou, X. 2020. Distribution route optimization for electric vehicles in urban cold chain logistics for fresh products under time-varying traffic conditions, Mathematical Problems in Engineering 2020: 9864935. https://doi.org/10.1155/2020/9864935
Zhou, Bo.; Wu, Y.; Zhou, Bi.; Wang, R.; Ke, W.; Zhang, S.; Hao, J. 2016. Real-world performance of battery electric buses and their life-cycle benefits with respect to energy consumption and carbon dioxide emissions, Energy 96: 603–613. https://doi.org/10.1016/j.energy.2015.12.041
Zhou, Y.; Chen, Z.; Zhang, J. 2017. Ranking vectors by means of the dominance degree matrix, IEEE Transactions on Evolutionary Computation 21(1): 34–51. https://doi.org/10.1109/tevc.2016.2567648
Zitzler, E.; Thiele, L., Laumanns, M.; Fonseca, C. M.; Da Fonseca, V. G. 2003. Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation 7(2): 117–132. https://doi.org/10.1109/tevc.2003.810758
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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