Choice-Based Demand Management and Vehicle Routing in E-fulfilment

Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, whilst keeping the significant delivery cost under control. To that end, the firm can try to influence customers when they are booking their delivery time slot so as to steer them towards choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for which time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location can lead to significantly increased profit as compared to current industry practice.