TY - GEN
T1 - A Deep Reinforcement Learning Approach for Crowdshipping Vehicle Routing Problem
AU - Huang, Hong
AU - Lin, Yu Sheng
AU - Kang, Jia Rong
AU - Lin, Chun Cheng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Extending the vehicle routing problem (VRP), the crowdshipping VRP (CVRP) considers crowdsourcing logistics. Crowdsourcing is flexible and convenient to reduce transportation costs and carbon emissions. However, crowdshipping requires to adapt to real-time changes such as road conditions and customer demands, which heuristic algorithms are not suitable for addressing these issues. Therefore, this study proposes a deep reinforcement learning (DRL) approach to react to real-time environmental changes to solve the CVRP. The CVRP considers a single depot and multiple transfer points to serve multiple customers, in which cargos can be delivered by either the vehicle directly, or crowdworkers after the vehicle stores cargos at transfer points. In the proposed DRL, the agent explores feasible decisions, and revises the path that it should take based on feedbacks. The cost effectiveness that affects crowdshipping includes the vehicle routing, and whether the concerned customer is suitable for crowdshipping. The experimental results show the efficiency and accuracy of the trained model for medium-sized VRPs are much higher than classical heuristic algorithms.
AB - Extending the vehicle routing problem (VRP), the crowdshipping VRP (CVRP) considers crowdsourcing logistics. Crowdsourcing is flexible and convenient to reduce transportation costs and carbon emissions. However, crowdshipping requires to adapt to real-time changes such as road conditions and customer demands, which heuristic algorithms are not suitable for addressing these issues. Therefore, this study proposes a deep reinforcement learning (DRL) approach to react to real-time environmental changes to solve the CVRP. The CVRP considers a single depot and multiple transfer points to serve multiple customers, in which cargos can be delivered by either the vehicle directly, or crowdworkers after the vehicle stores cargos at transfer points. In the proposed DRL, the agent explores feasible decisions, and revises the path that it should take based on feedbacks. The cost effectiveness that affects crowdshipping includes the vehicle routing, and whether the concerned customer is suitable for crowdshipping. The experimental results show the efficiency and accuracy of the trained model for medium-sized VRPs are much higher than classical heuristic algorithms.
KW - Vehicle routing problem
KW - crowdsourcing
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85146334392&partnerID=8YFLogxK
U2 - 10.1109/IEEM55944.2022.9989773
DO - 10.1109/IEEM55944.2022.9989773
M3 - Conference contribution
AN - SCOPUS:85146334392
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 598
EP - 599
BT - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
PB - IEEE Computer Society
T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
Y2 - 7 December 2022 through 10 December 2022
ER -