Location-routing Optimisation for Urban Logistics Using Mobile Parcel Locker Based on Hybrid Q-Learning Algorithm

TL;DR

This paper develops a hybrid Q-learning algorithm to optimize the placement and routing of mobile parcel lockers in urban logistics, significantly improving solution quality over traditional heuristics.

Contribution

It introduces a novel hybrid Q-learning based method for solving the location-routing problem for mobile parcel lockers, integrating global and local search strategies.

Findings

HQM outperforms genetic algorithms with 443.41% solution improvement

Route adjustment strategies effectively handle stochastic delays

Critical factors influencing service delays are identified and analyzed

Abstract

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.