Dynamic priority rules for combining on-demand passenger transportation and transportation of goods

Urban on-demand transportation services are booming in both passenger transportation and the transportation of goods. The types of service differ in timeliness and compensation and, until now, providers operate larger fleets separately for each type of service. While this may ensure sufficient resources for lucrative passenger transportation, the separation also leaves consolidation potentials untapped. In this paper, we propose combining both services in an anticipatory way that ensures high passenger service rates while simultaneously transporting a large number of goods. To this end, we introduce a dynamic priority policy that uses a time-dependent percentage of vehicles mainly to serve passengers. To find effective time-dependent parametrizations given a limited number of runtime-expensive simulations, we apply Bayesian optimization. We show that our anticipatory policy increases revenue and service rates significantly, whereas a myopic combination of service may actually lead to inferior performance com-pared to using two separate fleets.