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This paper investigates the problem of assigning shipping requests to ad hoccouriers in the context of crowdsourced urban delivery. The shipping requestsare spatially distributed each with a limited time window between the earliesttime for pickup and latest time for delivery. The ad hoc couriers, termedcrowdsourcees, also have limited time availability and carrying capacity. Wepropose a new deep reinforcement learning (DRL)-based approach to tackling thisassignment problem. A deep Q network (DQN) algorithm is trained which entailstwo salient features of experience replay and target network that enhance theefficiency, convergence, and stability of DRL training. More importantly, thispaper makes three methodological contributions: 1) presenting a comprehensiveand novel characterization of crowdshipping system states that encompassesspatial-temporal and capacity information of crowdsourcees and requests; 2)embedding heuristics that leverage the information offered by the staterepresentation and are based on intuitive reasoning to guide specific actionsto take, to preserve tractability and enhance efficiency of training; and 3)integrating rule-interposing to prevent repeated visiting of the same routesand node sequences during routing improvement, thereby further enhancing thetraining efficiency by accelerating learning. The effectiveness of the proposedapproach is demonstrated through extensive numerical analysis. The results showthe benefits brought by the heuristics-guided action choice andrule-interposing in DRL training, and the superiority of the proposed approachover existing heuristics in both solution quality, time, and scalability.Besides the potential to improve the efficiency of crowdshipping operationplanning, the proposed approach also provides a new avenue and genericframework for other problems in the vehicle routing context.

Deep Reinforcement Learning for Crowdsourced Urban Delivery: System  States Characterization, Heuristics-guided Action Choice, and  Rule-Interposing Integration