Journal of System Simulation
Abstract
Abstract: An intelligent agent technology architecture is adopted to the simulation model development of airport cargo business. Aiming at the optimization of airport cargo resources, a decision support system framework combining deep reinforcement learning (DRL) and airport cargo business simulation model is proposed. The simulated results are applied as the training data of the DRL network, and the DRL is used to optimize operation parameter of the simulation model. The mature system can be run online, which can provide optimized operation order in real time. In order to verify the effectiveness of the architecture, model development and experiments are conducted in Anylogic simulation platform, and the performances of DRL and OptQuest are compared. The results show that DRL can better optimize airport cargo business on the basis of ensuring orderly airport cargo operations.
Recommended Citation
Wang, Hongwei and Yang, Peng
(2022)
"Research on Optimization of Airport Cargo Business Based on Deep Reinforcement Learning,"
Journal of System Simulation: Vol. 34:
Iss.
3, Article 22.
DOI: 10.16182/j.issn1004731x.joss.20-0794
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss3/22
First Page
651
Revised Date
2021-01-03
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0794
Last Page
660
CLC
TP391.9
Recommended Citation
Hongwei Wang, Peng Yang. Research on Optimization of Airport Cargo Business Based on Deep Reinforcement Learning[J]. Journal of System Simulation, 2022, 34(3): 651-660.
DOI
10.16182/j.issn1004731x.joss.20-0794
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons