Learning Dynamic Selection and Pricing of Out-of-Home Deliveries
Fabian Akkerman Peter Dieter Martijn Mes
Home delivery failures, traffic congestion, and relatively large handling
times have a negative impact on the profitability of last-mile logistics. These
external factors contribute to up to $28\%$ of the overall costs and $25\%$ of
emissions for the home delivery supply chain. A potential solution, showing
annual growth rates up to $36\%$, is the delivery to parcel lockers or parcel
shops, denoted by out-of-home (OOH) delivery. In the academic literature,
models of customer behavior with respect to OOH delivery were so far limited to
deterministic settings, contrasting with the stochastic nature of actual
customer choices. We model the sequential decision-making problem of which OOH
location to offer against what incentive for each incoming customer, taking
into account future customer arrivals and choices. We propose Dynamic Selection
and Pricing of OOH (DSPO), an algorithmic pipeline that uses a novel
spatial-temporal state encoding as input to a convolutional neural network. We
demonstrate the performance of our method by benchmarking it against three
state-of-the-art approaches. Our extensive numerical study, guided by
real-world data, reveals that DSPO can save $20.8\%$ in costs compared to a
situation without OOH locations, $8.1\%$ compared to a static selection and
pricing policy, and $4.6\%$ compared to a state-of-the-art demand management
benchmark. We provide comprehensive insights into the complex interplay between
OOH delivery dynamics and customer behavior influenced by pricing strategies.
The implications of our findings suggest practitioners to adopt dynamic
selection and pricing policies as OOH delivery gains a larger market share.