Truck Routing Problem Considering Platooning and Drivers’ Breaks
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32847
Truck Routing Problem Considering Platooning and Drivers’ Breaks

Authors: Xiaoyuan Yan, Min Xu

Abstract:

Truck platooning refers to a convoy of digitally connected automated trucks traveling safely with a small inter-vehicle gap. It has been identified as one of the most promising and applicable technologies towards automated and sustainable freight transportation. Although truck platooning delivers significant energy-saving benefits, it cannot be realized without good coordination of drivers’ shifts to lead the platoons subject to their mandatory breaks. Therefore, this study aims to route a fleet of trucks to their destinations using the least amount of fuel by maximizing platoon opportunities under the regulations of drivers’ mandatory breaks. We formulate this platoon coordination problem as a mixed-integer linear programming problem and solve it by CPLEX. Numerical experiments are conducted to demonstrate the effectiveness and efficiency of our proposed model. In addition, we also explore the impacts of drivers’ compulsory breaks on the fuel-savings performance. The results show a slight increase in the total fuel costs in the presence of drivers’ compulsory breaks, thanks to driving-while-resting benefit provided for the trailing trucks. This study may serve as a guide for the operators of automated freight transportation.

Keywords: Truck platooning, route optimization, compulsory breaks, energy saving.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 524

References:


[1] Larsson, E., Sennton, G., Larson, J., “The vehicle platooning problem: Computational complexity and heuristics,” Transportation Research Part C: Emerging Technologies, 60, pp. 258-277, 2015.
[2] Bhoopalam, A.K., Agatz, N., Zuidwijk, R., “Planning of truck platoons: A literature review and directions for future research,” Transportation Research Part B: Methodological, 107, 212-228, 2018.
[3] Hu, J., Wang, H., Li, X., Li, X., “Modelling merging behaviour joining a cooperative adaptive cruise control platoon,” IET Intelligent Transport Systems, 14(7), pp. 693-701,2020.
[4] Liang, K.-Y., “Coordination and routing for fuel-efficient heavy-duty vehicle platoon formation,” KTH Royal Institute of Technology, 2014.
[5] Alam, A., Besselink, B., Turri, V., Mårtensson, J., Johansson, K.H., “Heavy-duty vehicle platooning for sustainable freight transportation: A cooperative method to enhance safety and efficiency,” IEEE Control Systems Magazine, 35(6), pp. 34-56,2015.
[6] Chen, J., Bai, D., Liang, H., Zhou, Y, “A third-order consensus approach for vehicle platoon with intervehicle communication,” Journal of Advanced Transportation,112, pp. 36-49, 2018.
[7] Jia, D., Ngoduy, D., “Platoon based cooperative driving model with consideration of realistic inter-vehicle communication,” Transportation Research Part C: Emerging Technologies, 68, pp. 245-264, 2016.
[8] Bonnet, C., Fritz, H., “Fuel consumption reduction in a platoon: Experimental results with two electronically coupled trucks at close spacing”, SAE Technical Paper,2000.
[9] Shladover, S.E., Lu, X.-Y., Yang, S., Ramezani, H., Spring, J., Nowakowski, C., Nelson, D., Thompson, D., Kailas, A., McAuliffe, B., “Partial Automation for Truck Platooning. United States”, Federal Highway Administration Office of Research, 120, pp. 326-360, 2019.
[10] Larson, J., Kammer, C., Liang, K.-Y., Johansson, K.H. “Coordinated route optimization for heavy-duty vehicle platoons”, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), IEEE, pp. 1196-1202.
[11] Luo, F., Larson, J., Munson, T., “Coordinated platooning with multiple speeds”, Transportation Research Part C: Emerging Technologies, 90, pp. 213-225, 2018.
[12] Boysen, N., Briskorn, D., Schwerdfeger, S., “The identical-path truck platooning problem”, Transportation Research Part B: Methodological, 109, pp. 26-39, 2018.
[13] Goel, A., “A mixed-integer programming formulation and effective cuts for minimizing schedule durations of Australian truck drivers”, Journal of Scheduling, 15(6), pp. 733-741, 2021.