TY - GEN
T1 - Dynamic Vehicle-Cargo Matching Based on Adaptive Time Windows
AU - Feng, Chong
AU - Liao, Jiajun
AU - Mao, Jiali
AU - Liu, Jiaye
AU - Guo, Ye
AU - Qian, Weining
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - The core task of vehicle-cargo matching is to dispatch the cargoes to the trucks. The existing matching policies mainly focus on maximizing the shipping weight for each truck. Due to each cargo is bulky and heavy in bulk logistics area, such strategies cannot ensure maximization of total weight of cargoes to be transported, and lead to a few cargoes be stranded. To tackle this issue, we present an intelligent decision framework for vehicle-cargo matching, called ILPD. Based on the limiting rules and features related to loading plan decisions that extracted from historical logistics data, we design a time window-based matching policy to achieve the goal of maximizing the total shipping weight and minimizing the quantity of stranded cargoes. Specifically, in each time window, dynamic programming and Branch-and-Bound method are leveraged to generate the loading plans of cargoes with the aim of minimization of stranded cargoes’ quantities. Then, Kuhn-Munkres algorithm is used to make the matching decisions to obtain maximum weight matching. Further, to fit for dynamic changing number of trucks and cargoes, a time zone-based Q-learning algorithm is proposed to adjust the time window size adaptively. Extensive experimental results on real data sets validate the effectiveness and practicality of our proposal.
AB - The core task of vehicle-cargo matching is to dispatch the cargoes to the trucks. The existing matching policies mainly focus on maximizing the shipping weight for each truck. Due to each cargo is bulky and heavy in bulk logistics area, such strategies cannot ensure maximization of total weight of cargoes to be transported, and lead to a few cargoes be stranded. To tackle this issue, we present an intelligent decision framework for vehicle-cargo matching, called ILPD. Based on the limiting rules and features related to loading plan decisions that extracted from historical logistics data, we design a time window-based matching policy to achieve the goal of maximizing the total shipping weight and minimizing the quantity of stranded cargoes. Specifically, in each time window, dynamic programming and Branch-and-Bound method are leveraged to generate the loading plans of cargoes with the aim of minimization of stranded cargoes’ quantities. Then, Kuhn-Munkres algorithm is used to make the matching decisions to obtain maximum weight matching. Further, to fit for dynamic changing number of trucks and cargoes, a time zone-based Q-learning algorithm is proposed to adjust the time window size adaptively. Extensive experimental results on real data sets validate the effectiveness and practicality of our proposal.
KW - Adaptive time window
KW - Steel logistics
KW - Task assignment
KW - Vehicle-cargo matching
UR - https://www.scopus.com/pages/publications/85151160553
U2 - 10.1007/978-3-031-25158-0_23
DO - 10.1007/978-3-031-25158-0_23
M3 - 会议稿件
AN - SCOPUS:85151160553
SN - 9783031251573
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 296
EP - 312
BT - Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings
A2 - Li, Bohan
A2 - Tao, Chuanqi
A2 - Yue, Lin
A2 - Han, Xuming
A2 - Calvanese, Diego
A2 - Amagasa, Toshiyuki
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022
Y2 - 25 November 2022 through 27 November 2022
ER -