Joint operation planning of drivers and trucks for semi-autonomous truck platooning

Abstract

AbstractSemi-autonomous truck platooning, a futuristic and promising traveling mode of trucks on highways, has received extensive attention as autonomous driving gains more maturity. Under the semi-autonomous platooning mode, rather than requiring a human driver for each truck in the platoon, only one human driver is needed for the leading truck, and all following trucks can be controlled by autonomous driving and wireless communication technology. Therefore, in addition to saving energy, such a technology can help reduce labor costs by using fewer drivers to fulfill the same demand. Compared to traditional truck platooning, the schedules of drivers under semi-autonomous truck platooning may not be exactly the same as trucks, which are usually neglected in recent studies. Therefore, in this paper, we make the first attempt to develop a mathematical modeling framework to optimise the operation plan of drivers and trucks interdependently. To be mentioned, the truck fleet is equipped with the semi-autonomous platooning function and time windows. Specifically, the operation plan will dictate the number of drivers to be dispatched, and traveling routes and time schedules of drivers and trucks interdependently with the objective to minimise the total operation cost, including drivers' fixed dispatch cost, on-road labor cost, and energy consumption cost while fulfilling all delivery demands. To tackle the large-scale problem in a timely manner, a tailored Lagrangian Relaxation approach is proposed to solve the model. Numerical experiments are conducted to demonstrate the performance of the proposed modeling framework and validate the feasibility and efficiency of the proposed solution algorithm.Keywords: Semi-autonomous truck platooningvehicle routing and schedulingdriver dispatchinglabor and energy costlagrangian relaxation Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partially supported by the National Natural Science Foundation of China (72101153, 72061127003, 72001021), Shanghai Chenguang Program (21CGA72), Shanghai Eastern Scholar Program (QD2020057), the Preparation Fund of Shanghai Key Laboratory of Urban Design and Urban Science (Grant No. 10407_Key Lab_Preparation Fund), the NYU Shanghai Boost Fund, and the NYU Shanghai Doctoral Fellowships.