  This paper investigates the problem of assigning shipping requests to ad hoc
couriers in the context of crowdsourced urban delivery. The shipping requests
are spatially distributed each with a limited time window between the earliest
time for pickup and latest time for delivery. The ad hoc couriers, termed
crowdsourcees, also have limited time availability and carrying capacity. We
propose a new deep reinforcement learning (DRL)-based approach to tackling this
assignment problem. A deep Q network (DQN) algorithm is trained which entails
two salient features of experience replay and target network that enhance the
efficiency, convergence, and stability of DRL training. More importantly, this
paper makes three methodological contributions: 1) presenting a comprehensive
and novel characterization of crowdshipping system states that encompasses
spatial-temporal and capacity information of crowdsourcees and requests; 2)
embedding heuristics that leverage the information offered by the state
representation and are based on intuitive reasoning to guide specific actions
to take, to preserve tractability and enhance efficiency of training; and 3)
integrating rule-interposing to prevent repeated visiting of the same routes
and node sequences during routing improvement, thereby further enhancing the
training efficiency by accelerating learning. The effectiveness of the proposed
approach is demonstrated through extensive numerical analysis. The results show
the benefits brought by the heuristics-guided action choice and
rule-interposing in DRL training, and the superiority of the proposed approach
over existing heuristics in both solution quality, time, and scalability.
Besides the potential to improve the efficiency of crowdshipping operation
planning, the proposed approach also provides a new avenue and generic
framework for other problems in the vehicle routing context.
