Autonomous truck platooning : scheduling, routing and personnel

Abstract

Autonomous trucks (ATs) are expected to be an effective solution to reducing the operating costs and carbon footprint in road freight transport. To realize the transition from a human-driving truck (HDT) feet to a complete unmanned truck feet, platooning with a driver in the leading vehicle is a practical concept that can be applied in this stage. To fully reap the benefits of cooperative autonomous truck platooning, this thesis proposes a hierarchical modeling framework to explicate the necessitated strategies. The first work formulates and analyzes an optimal ATs platooning schedule where a detour is possible. Decisions regarding the routing, platoon composition and scheduling are made simultaneously based on the minimal platoon-size dependent costs accounting for labor costs and fuel costs. We also propose a tailored combinatorial Benders decomposition algorithm to solve the model efficiently. Our numerical results show these techniques are effective in reducing computational complexity and time. We discuss the impacts of the number of ATs, the platoon size limit, and the ratio of fuel price and the driver wages on the performance of the AT platooning schedules based on the Hong Kong road network. The second part of the thesis investigates the potential of AT platooning to fight against driver shortage by considering the coordination between platooning schedules and the driver assignment under the driving hour regulations. The problem is considered in the setting of long-haul freight transport where ATs are deployed to service the mainline haulage. A branch and price algorithm embedded with column generation is developed to find the optimal solution to the formation of AT platoons, trip schedules and driver assignment. Given the transport requests, our model and algorithm can determine the minimal drivers to complete the tasks.