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A REVIEW ON REAL VEHICLE USAGE MODELLING OF DRIVERLESS MULTIPURPOSE VEHICLES IN VEHICLE ROUTING PROBLEMS

Published online by Cambridge University Press:  19 June 2023

Raphael Andreolli*
Affiliation:
KTH Royal Institute of Technology, Division of Vehicle Engineering and Solid Mechanics, Department of Engineering Mechanics, Stockholm, Sweden; Integrated Transport Research Lab (ITRL), KTH Royal Institute of Technology, Stockholm, Sweden; Centre for ECO2 Vehicle Design, KTH Royal Institute of Technology, Stockholm, Sweden; Scania CV AB, Södertälje, Sweden
Mikael Nybacka
Affiliation:
KTH Royal Institute of Technology, Division of Vehicle Engineering and Solid Mechanics, Department of Engineering Mechanics, Stockholm, Sweden; Integrated Transport Research Lab (ITRL), KTH Royal Institute of Technology, Stockholm, Sweden;
Ciarán J. O'Reilly
Affiliation:
KTH Royal Institute of Technology, Division of Vehicle Engineering and Solid Mechanics, Department of Engineering Mechanics, Stockholm, Sweden; Centre for ECO2 Vehicle Design, KTH Royal Institute of Technology, Stockholm, Sweden;
Erik Jenelius
Affiliation:
KTH Royal Institute of Technology, Department of Civil and Architectural Engineering, Stockholm, Sweden;
Eric Falkgrim
Affiliation:
Scania CV AB, Södertälje, Sweden
*
Andreolli, Raphael Gunnar Paulo, KTH Royal Institute of Technology, Sweden, [email protected]

Abstract

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Real vehicle usage rarely matches the predictions made during early phases of vehicle development and sales processes at commercial road vehicle manufacturers. The automotive industry needs multidisciplinary vehicle design methods to predict real-world vehicle operations by considering the vehicle level and the transport system level simultaneously, in a more holistic approach. The aim of this study was to analyse how realistic vehicle usage of driverless multipurpose vehicles can be modelled in Vehicle Routing Problems (VRPs) by conducting a systematic literature review. We found that real vehicle usage modelling of driverless multipurpose vehicles in VRPs mainly depended on the following elements: VRP variant, energy consumption model, energy consumption rate class, number of vehicle-specific design variables and transport system-level factors. Furthermore, we identified in the literature five classes of energy consumption rate edge behaviour in VRPs. These findings can support decision-making in the modelling process to select the most suitable combination of elements, and their level of detail for the overall modelling aim and purpose.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Abousleiman, R., Rawashdeh, O. and Boimer, R. (2017), “Electric Vehicles Energy Efficient Routing Using Ant Colony Optimization”, SAE International Journal of Alternative Powertrains, Vol. 6 No. 1, pp. 114, http://doi.org/10.4271/2017-01-9075.CrossRefGoogle Scholar
Bahrami, S., Nourinejad, M., Amiijamshidi, G. and Roorda, M.J. (2020), “The Plugin Hybrid Electric Vehicle routing problem: A power-management strategy model”, Transportation Research Part C: Emerging Technologies, Vol. 111, pp. 318333, http://doi.org/10.1016Zj.trc.2019.12.006.CrossRefGoogle Scholar
Basso, R., Kulcsar, B., Egardt, B., Lindroth, P. and Sanchez-Diaz, I. (2019), “Energy consumption estimation integrated into the Electric Vehicle Routing Problem”, Transportation Research Part D: Transport and Environment, Vol. 69, http://doi.org/10.1016/j∼.trd.2019.01.006.CrossRefGoogle Scholar
Basso, R., Kulcsar, B. and Sanchez-Diaz, I. (2021), “Electric vehicle routing problem with machine learning for energy prediction”, Transportation Research Part B: Methodological, Vol. 145, pp. 2455, http://doi.org/10.1016/j.trb.2020.12.007.CrossRefGoogle Scholar
Basso, R., Kulcsar, B., Sanchez-Diaz, I. and Qu, X. (2022), “Dynamic stochastic electric vehicle routing with safe reinforcement learning”, Transportation Research Part E: Logistics and Transportation Review, Vol. 157, http://doi.org/10.1016/j∼.tre.2021.102496.CrossRefGoogle Scholar
Basso, R., Lindroth, P., Kulcsar, B. and Egardt, B. (2016), “Traffic aware electric vehicle routing”, in: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), IEEE, Rio de Janeiro, Brazil, pp. 416421, http://doi.org/10.1109/ITSC.2016.7795588.CrossRefGoogle Scholar
Behnke, M. and Kirschstein, T. (2017), “The impact of path selection on GHG emissions in city logistics”, Transportation Research PartE: Logistics and Transportation Review, Vol. 106, pp. 320336, http://doi.org/10.1016/j.tre.2017.08.011.CrossRefGoogle Scholar
Bekta§, T. and Laporte, G. (2011), “The Pollution-Routing Problem”, Transportation Research Part B: Methodological, Vol. 45 No. 8, pp. 12321250, http://doi.org/10.1016Zj.trb.2011.02.004.CrossRefGoogle Scholar
Conrad, R.G. and Figliozzi, M.A. (2011), “The recharging vehicle routing problem”, in: 61st Annual IIE Conference and Expo Proceedings, Institute of Industrial Engineers, Reno, Nevada, United States.Google Scholar
Demir, E., Bekta§, T. and Laporte, G. (2011), “A comparative analysis of several vehicle emission models for road freight transportation”, Transportation Research Part D: Transport and Environment, Vol. 16 No. 5, pp. 347357, http://doi.org/10.1016/jj.trd.2011.01.011.CrossRefGoogle Scholar
Demir, E., Bekta§, T. and Laporte, G. (2012), “An adaptive large neighborhood search heuristic for the Pollution- Routing Problem”, European Journal of Operational Research, Vol. 223 No. 2, pp. 346359, http://doi.org/10.1016/j.ejor.2012.06.044.CrossRefGoogle Scholar
Demir, E., Bekta§, T. and Laporte, G. (2014), “A review of recent research on green road freight transportation”, European Journal of Operational Research, Vol. 237 No. 3, pp. 775793, http://doi.org/10.1016/j.ejor.2013.12.033.CrossRefGoogle Scholar
Ding, N., Yang, J., Han, Z. and Hao, J. (2022), “Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption”, Mathematics, Vol. 10 No. 17, http://doi.org/10.3390/math10173099.CrossRefGoogle Scholar
Dukkanci, O., Kara, B.Y. and Bekta§, T. (2019), “The green location-routing problem”, Computers & Operations Research, Vol. 105, pp. 187202, http://doi.org/10.1016/jxor.2019.01.011.CrossRefGoogle Scholar
Erdogan, S. and Miller-Hooks, E. (2012), “A Green Vehicle Routing Problem”, Transportation Research PartE: Logistics and Transportation Review, Vol. 48 No. 1, pp. 100114, http://doi.org/10.1016/jj.tre.2011.08.001.CrossRefGoogle Scholar
Figliozzi, M. (2010), “Vehicle Routing Problem for Emissions Minimization”, Transportation Research Record, No. 2197, pp. 17, http://doi.org/10.3141/2197-01.CrossRefGoogle Scholar
Franceschetti, A., Honhon, D., Van Woensel, T., Bekta§, T. and Laporte, G. (2013), “The time-dependent pollution- routing problem”, Transportation Research PartB: Methodological, Vol. 56, pp. 265293, http://doi.org/10.1016/j.trb.2013.08.008.CrossRefGoogle Scholar
Ghandriz, T., Jacobson, B., Islam, M., Hellgren, J. and Laine, L. (2021), “Transportation-Mission-Based Optimization of Heterogeneous Heavy-Vehicle Fleet Including Electrified Propulsion”, Energies, Vol. 14 No. 11, http://doi.org/10.3390/en14113221.CrossRefGoogle Scholar
Goeke, D. and Schneider, M. (2015), “Routing a mixed fleet of electric and conventional vehicles”, European Journal of Operational Research, Vol. 245 No. 1, pp. 8199, http://doi.org/10.1016/jj.ejor.2015.01.049.CrossRefGoogle Scholar
Hatzenbuhler, J. (2022), Simulation and optimization of innovative urban transportation systems, Dissertation, KTH Royal Institute of Technology, Stockholm, Sweden.Google Scholar
Hulagu, S. and Celikoglu, H.B. (2022), “Electric Vehicle Location Routing Problem with Vehicle Motion Dynamics-Based Energy Consumption and Recovery”, IEEE Transactions on Intelligent Transportation Systems, Vol. 23 No. 8, pp. 1027510286, http://doi.org/10.1109/TITS.2021.3089675.CrossRefGoogle Scholar
Jesson, J.K., Matheson, L. and Lacey, F.M. (2011), Doing your literature review: traditional and systematic techniques, SAGE Publications Ltd, London.Google Scholar
Kancharla, S.R. and Ramadurai, G. (2018a), “An Adaptive Large Neighborhood Search Approach for Electric Vehicle Routing with Load-Dependent Energy Consumption”, Transportation in Developing Economies, Vol. 4 No. 2, http://doi.org/10.1007/s40890-018-0063-3.CrossRefGoogle Scholar
Kancharla, S.R. and Ramadurai, G. (2018b), “Incorporating driving cycle based fuel consumption estimation in green vehicle routing problems”, Sustainable Cities and Society, Vol. 40, pp. 214221, http://doi.org/10.1016/j.scs.2018.04.016.CrossRefGoogle Scholar
Kara, I., Kara, B.Y. and Yetis, M.K. (2007), “Energy Minimizing Vehicle Routing Problem”, in: Dress, A., Xu, Y. and Zhu, B. (Editors), Combinatorial Optimization and Applications, Vol. 4616, Springer Verlag, Xi'an, China, pp. 6271, http://doi.org/10.1007/978-3-540-73556-4_9.CrossRefGoogle Scholar
Kog, C., Bekta§, T., Jabali, O. and Laporte, G. (2014), “The fleet size and mix pollution-routing problem”, Transportation Research Part B: Methodological, Vol. 70, pp. 239254, http://doi.org/10.1016/jj.trb.2014.09.008.Google Scholar
Kopfer, H. and Vornhusen, B. (2019), “Energy vehicle routing problem for differently sized and powered vehicles”, Journal of Business Economics, Vol. 89 No. 7, pp. 793821, http://doi.org/10.1007/s11573-018-0910-z.CrossRefGoogle Scholar
Krebs, C. and Ehmke, J.F. (2021), “Axle Weights in Combined Vehicle Routing and Container Loading Problems”, EURO Journal on Transportation and Logistics, Vol. 10, http://doi.org/10.1016/jxjtl.2021.100043.CrossRefGoogle Scholar
Macrina, G., Di Puglia Pugliese, L., Guerriero, F. and Laporte, G. (2019a), “The green mixed fleet vehicle routing problem with partial battery recharging and time windows”, Computers & Operations Research, Vol. 101, pp. 183199, http://doi.org/10.1016/jj.cor.2018.07.012.CrossRefGoogle Scholar
Macrina, G., Laporte, G., Guerriero, F. and Di Puglia Pugliese, L. (2019b), “An energy-efficient green-vehicle routing problem with mixed vehicle fleet, partial battery recharging and time windows”, European Journal of Operational Research, Vol. 276 No. 3, pp. 971982, http://doi.org/10.1016/jj.ejor.2019.01.067.CrossRefGoogle Scholar
Montoya, A., Gueret, C., Mendoza, J.E. and Villegas, J.G. (2017), “The electric vehicle routing problem with nonlinear charging function”, Transportation Research PartB: Methodological, Vol. 103, pp. 87110, http://doi.org/10.1016/j.trb.2017.02.004.CrossRefGoogle Scholar
Murakami, K. (2017), “A new model and approach to electric and diesel-powered vehicle routing”, Transportation Research Part E: Logistics and Transportation Review, Vol. 107, pp. 2337, http://doi.org/10.1016/j.tre.2017.09.004.CrossRefGoogle Scholar
O'Reilly, C.J., Goransson, P., Funazaki, A., Suzuki, T., Edlund, S., Gunnarsson, C., Lundow, J.O., Cerin, P., Cameron, C.J., Wennhage, P. and Potting, J. (2016), “Life cycle energy optimisation: A proposed methodology for integrating environmental considerations early in the vehicle engineering design process”, Journal of Cleaner Production, Vol. 135, pp. 750759, http://doi.org/10.1016/jjclepro.2016.06.163.CrossRefGoogle Scholar
Pelletier, S., Jabali, O. and Laporte, G. (2019), “The electric vehicle routing problem with energy consumption uncertainty”, Transportation Research Part B: Methodological, Vol. 126, pp. 225255, http://doi.org/10.1016/j.trb.2019.06.006.CrossRefGoogle Scholar
Rastani, S. and Catay, B. (2021), “A large neighborhood search-based matheuristic for the load-dependent electric vehicle routing problem with time windows”, Annals of Operations Research, http://doi.org/10.1007/s10479-021-04320-9.Google Scholar
Rastani, S., Yuksel, T. and Catay, B. (2019), “Effects of ambient temperature on the route planning of electric freight vehicles”, Transportation Research Part D: Transport and Environment, Vol. 74, pp. 124141, http://doi.org/10.1016/j.trd.2019.07.025.CrossRefGoogle Scholar
Romano, L., Johannesson, P., Nordstrom, E., Bruzelius, F., Andersson, R. and Jacobson, B. (2022), “A classification method of road transport missions and applications using the operating cycle format”, IEEE Access, pp. 7308773121, http://doi.org/10.1109/ACCESS.2022.3188872.CrossRefGoogle Scholar
Ulrich, C., Friedrich, H., Weimer, J. and Schmid, S. (2019), “New operating strategies for an on-the-road modular, electric and autonomous vehicle concept in urban transportation”, World Electric Vehicle Journal, Vol. 10 No. 4, http://doi.org/10.3390/wevj10040091.CrossRefGoogle Scholar
Vidal, T., Laporte, G. and Matl, P. (2020), “A concise guide to existing and emerging vehicle routing problem variants”, European Journal of Operational Research, Vol. 286 No. 2, pp. 401416, http://doi.org/10.1016/j.ejor.2019.10.010.CrossRefGoogle Scholar
Xiao, Y., Zhang, Y., Kaku, I., Kang, R. and Pan, X. (2021), “Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption”, Renewable and Sustainable Energy Reviews, Vol. 151, http://doi.org/10.1016/jj.rser.2021.111567.CrossRefGoogle Scholar
Yu, V.F., Jodiawan, P. and Gunawan, A. (2021), “An Adaptive Large Neighborhood Search for the green mixed fleet vehicle routing problem with realistic energy consumption and partial recharges”, Applied Soft Computing, Vol. 105, http://doi.org/10.1016/jj.asoc.2021.107251.CrossRefGoogle Scholar
Zang, Y., Wang, M. and Qi, M. (2022), “A column generation tailored to electric vehicle routing problem with nonlinear battery depreciation”, Computers & Operations Research, Vol. 137, http://doi.org/10.1016/jxor.2021.105527.CrossRefGoogle Scholar
Zhang, S., Gajpal, Y., Appadoo, S. and Abdulkader, M. (2018), “Electric vehicle routing problem with recharging stations for minimizing energy consumption”, International Journal of Production Economics, Vol. 203, http://doi.org/10.1016/jj.ijpe.2018.07.016.CrossRefGoogle Scholar