  Mobile parcel lockers (MPLs) have been recently introduced by urban logistics
operators as a means to reduce traffic congestion and operational cost. Their
capability to relocate their position during the day has the potential to
improve customer accessibility and convenience (if deployed and planned
accordingly), allowing customers to collect parcels at their preferred time
among one of the multiple locations. This paper proposes an integer programming
model to solve the Location Routing Problem for MPLs to determine the optimal
configuration and locker routes. In solving this model, a Hybrid Q-Learning
algorithm-based Method (HQM) integrated with global and local search mechanisms
is developed, the performance of which is examined for different problem sizes
and benchmarked with genetic algorithms. Furthermore, we introduced two route
adjustment strategies to resolve stochastic events that may cause delays. The
results show that HQM achieves 443.41% improvement on average in solution
improvement, compared with the 94.91% improvement of heuristic counterparts,
suggesting HQM enables a more efficient search for better solutions. Finally,
we identify critical factors that contribute to service delays and investigate
their effects.
