Dynamic On-Demand Crowdshipping Using Constrained and Heuristics-Embedded Double Dueling Deep Q-Network

Abstract

This paper proposes a deep reinforcement learning (DRL)-based approach to the dynamic on-demand crowdshipping problem in which requests constantly arrive in a crowdshipping system for pickup and delivery within limited time windows. The request pickup and delivery are performed by crowdsourcees, who are ordinary people dynamically arriving in and leaving the crowdshipping system, and dedicating their limited and heterogeneous available time and carrying capacity to crowdshipping. In return, crowdsourcees get paid by the delivery service provider who periodically assigns requests to crowdsourcees in the course of a day to minimize shipping cost. We adopt heuristics-embedded Deep Q-Network (DQN) algorithms that incorporate double and dueling structures, to train DRL agents. The idea of heuristics-embedded training is conceived by designing an elaborate action space where several refined local search heuristics are embedded to direct the specific action to take once an action type is chosen by DRL, with the purpose of preserving tractability of DRL training. To tackle the hard constraints pertaining to crowdsourcee and request time windows, we propose and integrate three new strategies (feasibility enforced local search, multiple schedules with different penalties, and exponential penalty) as part of the DRL training and testing. Extensive numerical analysis is conducted and shows that Double Dueling DQN with the exponential penalty strategy demonstrates the best performance. We compare the performance of the agent trained by Double Dueling DQN with conventional heuristic approaches, and find that the agent yields total shipping costs that are on average 24–37% lower than the conventional heuristic approaches. For problem instances that can be solved to optimality, the optimality gap using the trained agent is also quite small, in the range of 3–7%. Moreover, the trained agent is robust to stationary/non-stationary demand patterns. Lastly, our approach is further compared with a recent study that uses heuristics-embedded DQN, and shows superior performance (total shipping costs on average 19% lower) as a result of several differences.