  Crowdsourced delivery platforms face complex scheduling challenges to match
couriers and customer orders. We consider two types of crowdsourced couriers,
namely, committed and occasional couriers, each with different compensation
schemes. Crowdsourced delivery platforms usually schedule committed courier
shifts based on predicted demand. Therefore, platforms may devise an offline
schedule for committed couriers before the planning period. However, due to the
unpredictability of demand, there are instances where it becomes necessary to
make online adjustments to the offline schedule. In this study, we focus on the
problem of dynamically adjusting the offline schedule through shift extensions
for committed couriers. This problem is modeled as a sequential decision
process. The objective is to maximize platform profit by determining the shift
extensions of couriers and the assignments of requests to couriers. To solve
the model, a Deep Q-Network (DQN) learning approach is developed. Comparing
this model with the baseline policy where no extensions are allowed
demonstrates the benefits that platforms can gain from allowing shift
extensions in terms of reward, reduced lost order costs, and lost requests.
Additionally, sensitivity analysis showed that the total extension compensation
increases in a nonlinear manner with the arrival rate of requests, and in a
linear manner with the arrival rate of occasional couriers. On the compensation
sensitivity, the results showed that the normal scenario exhibited the highest
average number of shift extensions and, consequently, the fewest average number
of lost requests. These findings serve as evidence of the successful learning
of such dynamics by the DQN algorithm.
