TY - GEN
T1 - An order control policy in crowdsourced parcel pickup and delivery service
AU - Kang, Yuncheol
N1 - Funding Information:
Acknowledgements. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2017R1C1B1005354).
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2017R1C1B1005354).
Publisher Copyright:
© 2018, IFIP International Federation for Information Processing.
PY - 2018
Y1 - 2018
N2 - Crowdsourced parcel delivery service has progressed dramatically by actively incorporating innovative technologies and ideas. Yet, maximizing profitability of this new type of delivery service becomes another challenge for service providers as market grows. In this paper we study a service order control policy to maximize profitability from a service provider perspective. Specifically, we suggest an order admission control approach that determines acceptance or rejection of an incoming order according to its profitability characteristics. For this, we model the problem as an average reward Semi-Markov Decision Process and utilize reinforcement learning to obtain an optimal order control policy that maximizes overall profitability of a service provider. Through numerical illustrations, we show that our suggested approach outperforms traditional methods, especially when the order arrival rate is high. Thus, smart order management is an important component of parcel pickup and delivery services.
AB - Crowdsourced parcel delivery service has progressed dramatically by actively incorporating innovative technologies and ideas. Yet, maximizing profitability of this new type of delivery service becomes another challenge for service providers as market grows. In this paper we study a service order control policy to maximize profitability from a service provider perspective. Specifically, we suggest an order admission control approach that determines acceptance or rejection of an incoming order according to its profitability characteristics. For this, we model the problem as an average reward Semi-Markov Decision Process and utilize reinforcement learning to obtain an optimal order control policy that maximizes overall profitability of a service provider. Through numerical illustrations, we show that our suggested approach outperforms traditional methods, especially when the order arrival rate is high. Thus, smart order management is an important component of parcel pickup and delivery services.
KW - Admission control
KW - Crowdsourced parcel delivery
KW - Planning and decision-makings
KW - Reinforcement learning
KW - Smart logistics
UR - http://www.scopus.com/inward/record.url?scp=85053265754&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-99707-0_21
DO - 10.1007/978-3-319-99707-0_21
M3 - Conference contribution
AN - SCOPUS:85053265754
SN - 9783319997063
T3 - IFIP Advances in Information and Communication Technology
SP - 164
EP - 171
BT - Advances in Production Management Systems. Smart Manufacturing for Industry 4.0 - IFIP WG 5.7 International Conference, APMS 2018, Proceedings
A2 - Lee, Gyu M.
A2 - Kiritsis, Dimitris
A2 - von Cieminski, Gregor
A2 - Moon, Ilkyeong
A2 - Park, Jinwoo
PB - Springer New York LLC
T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2018
Y2 - 26 August 2018 through 30 August 2018
ER -