The paper investigates a stochastic-dynamic customer-oriented dial-a-ride problem of a ridepooling provider in a large city. Due to competing mobility services, the ridepooling provider needs to offer the customers short waiting and travel times. New customer requests are inserted to maximize their acceptance probability which depends on the customer’s detour in relation to their direct travel time. The trade-off between current and future customers is included by a “potential” that assesses the attractiveness of a tour for future customers. As we need to compute offers quickly, we introduce several precomputation steps, which are done in the meantime between two customer requests, as well as a clustering and a sampling method to reduce the computational effort and use parallelization. These methods allow to solve instances with larger numbers of customers. Our methods are evaluated in a comprehensive computational study with 2700 instances. The main result is that it is best to weight the interests of the current and the future customers equally. Furthermore, the approach can be used by the provider to steer the system’s performance. Given a desired service level, i.e. an acceptance rate, the provider can use the approach to decide on the required number of vehicles to reach this service level. Besides, the length of the delivery time windows is important to solve the trade-off between a sufficiently high degree of freedom to reinsert accepted customers in favour of new customers and the system’s attractiveness for accepted customers.
Schulz et al. (Mon,) studied this question.