Thesis Student

An extension to the fleet-size and mix-vehicle routing problem for a fleet of electric heavy vehicles


Background


With the current trends of electrification of commercial heavy vehicles, mission planning and vehicle transport optimization are becoming more important because of inherent characteristics of electric vehicles, e.g., range-anxiety. It means that there is not only the objective on fuel consumption reduction or shortest distance, but also the existence of complicating constraints on the vehicle range, and the consideration of additional costs caused by visits to the charging stations, including waiting times, and additional driver wages. Therefore, the planning of electric vehicles’ missions plays a larger role and is more complicated than the conventional vehicles.


A class of problems that can be suitable for planning the fleet mission is fleet-size and mix-vehicle routing problem (FSMVRP), which is extensively studied in the literature. A heterogeneous, or mixed, fleet refers to vehicles with different payload capacities (see [1]). In addition, this thesis aims at including vehicles with different powertrain, i.e., different battery sizes and ranges (e.g., [2]), and the fleet size being nonfixed.


Moreover, this thesis starts from a problem definition that is more general than the traditional routing problem and FSMVRP. For example, we will allow for multiple trips, i.e., a vehicle can travel on one or more cyclic routes more than once [3]; a node is allowed to be visited by the same or other vehicles more than once, i.e., split pick-ups and deliveries [4]; some of the nodes have charging stations that might or might not be visited [5]; at loading-unloading nodes, there is a constraint of the type of loading-unloading [3], so not all vehicles can visit all nodes. The demands at nodes can be pick-up or delivery or both and they may exceed vehicle capacities [6].


Purpose


The purpose of this thesis can be summarized as follows.
Designing an optimization model for minimizing the fleet transportation cost comprising electric energy cost and driver wage (or equivalent vehicle up-time cost) to find the best fleet size and composition and missions, i.e., routes, visited nodes, amounts of pick-ups and deliveries, visited charging stations, and number of trips to meet the network daily demand. The number of vehicles visiting a node, or a charging station is limited. For calculating energy consumption, a given on-road dynamic vehicle model shall be used considering road topography, vehicle powertrain and vehicle starts and stops.
Suggesting classical optimization solution methods, such as Benders or column generation reformulations, to decompose and solve the extended FSMVRP. The suggested methods need to be tested on a benchmark problem with a low number of nodes and vehicle types, in order to be solved within a reasonable computation time.



Additional Information


The thesis is recommended for one or two students with a strong background in mathematics and mathematical optimization with good programming skills. Prior experience with programming in Python, control theory, and modeling/simulation is meritorious. The work will be carried out at Volvo Group Trucks Technology, Sweden. Location: Preferably Göteborg, Sweden
Time: Preferably Jan 2023- Jun 2023, but we are flexible to adapt to your time schedule


Contact persons


Ingmar Bengtsson – Volvo GTT
email: ingmar.bengtsson@volvo.com


Leo Laine – Volvo GTT
email: leo.laine@volvo.com


Gabriel Ibanez – Volvo GTT
email: gabriel.ibanez@volvo.com


Kindly note that due to GDPR, we will not accept applications via mail. Please use our career site.


Bibliography


See the list of bibliography for related articles.


[1] Koc C¸ , Bektas T, Jabali O, Laporte G (2016) Thirty years of heterogeneous vehicle routing. European Journal of Operational Research 249(1):1–21 URL http://dx.doi.org/10.1016/j.tranpol.2011.12.006


[2] Kopfer H, Vornhusen B (2017) Energy vehicle routing problem for differently sized and powered vehicles. Journal of Business Economics pp 1–29 URL http://dx.doi.org/10.1007/s11573-018-0910-z1016/j.ejor.2015.07.020


[3] T. Ghandriz, B. J. H. Jacobson, M. Islam, J. Hellgren, and L. Laine, ‘Transportation-mission-based Optimization of Heterogeneous Heavy-vehicle Fleet Including Electrified Propulsion’, Energies, vol. 14, no. 11, 2021, doi:10.3390/en14113221 URL https://www.mdpi.com/1996-1073/14/11/3221


[4] Tavakkoli-Moghaddam R, Safaei N, Kah M, Rabbani M (2007) A new capacitated vehicle routing problem with split service for minimizing fleet cost by simulated annealing. Journal of the Franklin Institute 344(5):406–425 URL http://dx.doi.org/10.1016/j.jfranklin.2005.12.002


[5] Arslan O, Kara¸san OE (2016) A Benders decomposition approach for the charging station location problem with plug-in hybrid electric vehicles. Transportation Research Part B: Methodological 93:670–695 URL http://dx.doi.org/10.1016/j.trb.2016.09.001


[6] Teodorovic D, Krcmar-Nozic E, Pavkovic G (1995) The mixed fleet stochastic vehicle routing problem. Transportation Planning and Technology 19(1):31–43, URL http://dx.doi.org/10.1080/03081069508717556


[7] Ghandriz, Toheed. Transportation Mission-Based Optimization of Heavy Combination Road Vehicles and Distributed Propulsion, Including Predictive Energy and Motion Control. PhD thesis Chalmers Tekniska Högskola (Sweden), 2020. URL https://research.chalmers.se/en/publication/520358