A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching

Xinan Chen, Ruibin Bai, Rong Qu, Haibo Dong, Jianjun Chen

Abstract: International and domestic maritime trade has been expanding dramatically in the 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 twentyfoot equivalent unit containers, or TEUs) is the most important objective to improve. This paper proposes a genetic programming approach to build a dynamic 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.