  The demand for same-day delivery (SDD) has increased rapidly in the last few
years and has particularly boomed during the COVID-19 pandemic. The fast growth
is not without its challenge. In 2016, due to low concentrations of memberships
and far distance from the depot, certain minority neighborhoods were excluded
from receiving Amazon's SDD service, raising concerns about fairness. In this
paper, we study the problem of offering fair SDD-service to customers. The
service area is partitioned into different regions. Over the course of a day,
customers request for SDD service, and the timing of requests and delivery
locations are not known in advance. The dispatcher dynamically assigns vehicles
to make deliveries to accepted customers before their delivery deadline. In
addition to the overall service rate (utility), we maximize the minimal
regional service rate across all regions (fairness). We model the problem as a
multi-objective Markov decision process and develop a deep Q-learning solution
approach. We introduce a novel transformation of learning from rates to actual
services, which creates a stable and efficient learning process. Computational
results demonstrate the effectiveness of our approach in alleviating unfairness
both spatially and temporally in different customer geographies. We also show
this effectiveness is valid with different depot locations, providing
businesses with an opportunity to achieve better fairness from any location.
Further, we consider the impact of ignoring fairness in service, and results
show that our policies eventually outperform the utility-driven baseline when
customers have a high expectation on service level.
