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A reinforcement learning framework for improving parking decisions in last-mile delivery
Muriel,Juan E. Zhang,Lele Fransoo,Jan C. Villegas,Juan G.
Muriel,Juan E.
Zhang,Lele
Fransoo,Jan C.
Villegas,Juan G.
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.
Description
Publisher Copyright: © 2024 Hong Kong Society for Transportation Studies Limited.
Date
2024-04
Journal Title
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Volume Title
Publisher
Research Projects
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Journal Issue
Keywords
last-mile delivery, urban logistics, reinforcement learning, loading zone, simulation-optimisation, SDG 9 - Industry, Innovation, and Infrastructure, SDG 11 - Sustainable Cities and Communities
Citation
Muriel, J E, Zhang, L, Fransoo, J C & Villegas, J G 2024, 'A reinforcement learning framework for improving parking decisions in last-mile delivery', Transportmetrica B-Transport Dynamics, vol. 12, no. 1, 2337216. https://doi.org/10.1080/21680566.2024.2337216
License
info:eu-repo/semantics/openAccess