Daily load planning under different autonomous truck deployment scenarios. (October 2022) - Al Hajj Hassan, Lama; Hewitt, Mike; Mahmassani, Hani S.
Highlights: We consider a LTL trucking network with mixed fleet of autonomous and human-driven trucks. A methodology for daily load planning of mixed fleet LTL trucks is presented. Illustrative application with actual industry data is presented. Operational strategies for integrating autonomous trucks are formulated and tested. The ability to operate trucks entirely without drivers can yield significant cost reductions. Abstract: This paper presents and tests modified service network design formulations that account for five levels of truck automation in a daily load planning setting. Given daily updates of load information, the paths for the five deployment scenarios are adjusted using two daily updating strategies. Both strategies start with a base plan in which paths are generated based on the historic daily distribution of load dispatches during an average week. The two strategies are: (1) Option 1: re-optimization of pre-booked loads and new requests, and (2) Option 2: optimization of new requests only. The solutions of the two strategies are compared to the hindsight plan which assumes complete information of actual requests placed. The presented formulations are tested out on an industry partner's network. Results show that the savings achieved with re-optimization (Option 1) compared to insertion (Option 2) increase with more demand variability; this outcome is consistent across all fleet mixes. When most of the loads are new arrivals, the computational time of the two approaches is comparable and insertion is less attractive than re-optimization. With daily re-optimization, most of the plan changes adjust the terminals visited by a load compared to just changing the dispatch and arrival times along the load's path.