- author = "Xinan Chen and Ruibin Bai and Rong Qu and Haibo Dong and Jianjun Chen",
- title = "A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching",
- booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020",
- year = "2020",
- editor = "Yaochu Jin",
- pages = "paper id24651",
- address = "internet",
- month = "19-24 " # jul,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, container terminal, lorry, truck dispatching, dynamic",
- isbn13 = "978-1-7281-6929-3",
-
URL = "
http://www.cs.nott.ac.uk/~pszrq/files/CEC2020HGP.pdf",
-
DOI = "
doi:10.1109/CEC48606.2020.9185659",
- size = "8 pages",
- abstract = "International and domestic maritime trade has been expanding dramatically in last few decades, seaborne container transportation has become an indispensable part of maritime trade efficient and easy-to-use containers. As an important hub of container transport, container terminals use a range of metrics to measure their efficiency, among which the hourly container throughput (i.e., the number of twenty-foot equivalent unit containers, or TEUs) is the most important objective to improve. We propose a genetic programming approach to build adynamic truck dispatching system trained on real-world stochastic operations data. The experimental results demonstrated the superiority of this dynamic approach and the potential for practical applications.",
-
notes = "https://wcci2020.org/
University of Nottingham Ningbo China, China; University of Nottingham, United Kingdom; Xi'an Jiaotong-Liverpool University, China",
