Green transport fleet renewal using approximate dynamic programming: A case study in German heavy-duty road transportation

Governments and manufacturers are starting to enforce the European transport industry's transition to sustainable mobility. Meanwhile, transport companies have begun to set their own emissions goals. To achieve these sustainably, they must develop efficient policies to renew their fleets with alternative-fuel vehicles. However, since future trends in relevant parameters are highly uncertain, fleet managers struggle to make informed decisions. We formulate fleet renewal as a sequential optimization problem, considering multiple technologies and operational clusters. Vehicle purchase, sales, depreciation, fuel, carbon, and electric battery prices are modeled as stochastic variables. We propose approximate dynamic programming to calculate fleet renewal policies that achieve emissions goals while optimizing total costs of ownership. This approach is tested in a case study of a German logistics service provider. We investigate optimal timings of purchases and sales for a heavy-duty truck fleet, considering four drivetrain technologies. Our approach can guide decision making in various fleet renewal settings. By applying it to the case study, we derive important managerial implications. The mobility transition will significantly increase transport fleets' total cost of ownership. To minimize costs, companies should not move prematurely to low-emissions technologies, but hold vehicles for as long as possible to benefit from fewer purchases and sinking prices. The optimal policy depends on the distance driven. For short-distance operations, diesel trucks will remain the dominant technology in the next years, but will be replaced by battery electric trucks in the medium term. In the far future, trucks powered by electricity and hydrogen will be equally important.