TY - GEN
T1 - Neural Network Assisted Genetic Programming in Dynamic Container Port Truck Dispatching
AU - Chen, Xinan
AU - Bao, Feiyang
AU - Qu, Rong
AU - Dong, Jing
AU - Bai, Ruibin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Efficient truck dispatching is crucial for container port operations. Dynamic container port truck dispatching, a complex online optimization problem, poses significant challenges due to its uncertain and non-linear nature. This paper presents a novel neural network assisted genetic programming (NN-GP) approach, which combines the global search of genetic programming (GP) and the local search of recurrent neural network (RNN). In this framework, the RNN further refines GP individuals after genetic operations (crossovers and mutations), enhancing solution adaptability and precision in response to dynamic and uncertain scenarios. The proposed method leverages RNN's understanding of temporal dynamics and GP's robust exploration of the solution space, effectively addressing the dynamic container truck dispatching problem. Experiments using real-world container port data demonstrate that the RNN-GP model outperforms traditional heuristic methods and standalone GP algorithms, reducing dispatching time and increasing port efficiency. This research highlights the potential of hybridizing machine learning techniques with GP in solving complex real-world optimization problems.
AB - Efficient truck dispatching is crucial for container port operations. Dynamic container port truck dispatching, a complex online optimization problem, poses significant challenges due to its uncertain and non-linear nature. This paper presents a novel neural network assisted genetic programming (NN-GP) approach, which combines the global search of genetic programming (GP) and the local search of recurrent neural network (RNN). In this framework, the RNN further refines GP individuals after genetic operations (crossovers and mutations), enhancing solution adaptability and precision in response to dynamic and uncertain scenarios. The proposed method leverages RNN's understanding of temporal dynamics and GP's robust exploration of the solution space, effectively addressing the dynamic container truck dispatching problem. Experiments using real-world container port data demonstrate that the RNN-GP model outperforms traditional heuristic methods and standalone GP algorithms, reducing dispatching time and increasing port efficiency. This research highlights the potential of hybridizing machine learning techniques with GP in solving complex real-world optimization problems.
UR - http://www.scopus.com/inward/record.url?scp=85186502125&partnerID=8YFLogxK
U2 - 10.1109/ITSC57777.2023.10422513
DO - 10.1109/ITSC57777.2023.10422513
M3 - Conference contribution
AN - SCOPUS:85186502125
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2246
EP - 2251
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Y2 - 24 September 2023 through 28 September 2023
ER -