A reinforcement learning framework for improving parking decisions in last-mile delivery

Abstract

This study leverages simulation-optimisation with a Reinforcement Learning (RL) model to analyse the routing behaviour of delivery vehicles (DVs). We conceptualise the system as a stochastic k-armed bandit problem, representing a sequential interaction between a learner (the DV) and its surrounding environment. Each DV is assigned a random number of customers and an initial delivery route. If a loading zone is unavailable, the RL model is used to select a delivery strategy, thereby modifying its route accordingly. The penalty is gauged by the additional trucking and walking time incurred compared to the originally planned route. Our methodology is tested on a simulated network featuring realistic traffic conditions and a fleet of DVs employing four distinct lastmile delivery strategies. The results of our numerical experiments underscore the advantages of providing DVs with an RL-based decision support system for en-route decision-making, yielding benefits to the overall efficiency of the transport network.

Highlights

• Combining simulation and optimisation algorithms with reinforcement learning

• Model DVs en-route parking decisions with a k-armed bandit algorithm

• Evaluating the impacts of delivery strategies on traffic congestion and in last-mile delivery efficiency

KEYWORDS:

• Last-mile delivery
• urban logistics
• reinforcement learning
• loading zone
• simulation-optimisation

Disclosure statement

No potential conflict of interest was reported by the author(s).