| dc.description.abstract | The primary aim of this Master′s thesis is to investigate how to apply deep learning techniques effectively to solve the air cargo loading optimization problem. Air cargo loading optimization plays a key role in reducing operating costs and enhancing market competitiveness. However, due to the diversity of the cargo, such as size, weight, and specific requirements, the loading optimization problem demonstrates high-dimensionality and complexity, which are the challenges this research has to face.
To solve this problem, this research proposes a deep neural network model based on the Self-Attention mechanism. This model has the capability to effectively identify the best or near-optimal cargo loading strategy, aiming to maximize the number of cargo loaded in the limited cabin space, while ensuring the safety and reliability of the transportation process.
This paper first provides an in-depth analysis and discussion of the air cargo loading optimization problem and its significance in both business and scientific research. Following this, we give a comprehensive introduction to deep learning technologies, covering topics such as deep neural networks and the Self-Attention mechanism. Then, we propose a deep neural network model based on the Self-Attention mechanism and provide a detailed mathematical analysis and model design. We offer the theoretical foundation of this model and explain how to adjust the model parameters according to the specific demands and conditions of the problem.
Lastly, we verify the performance of the proposed model in the air cargo loading optimization problem through a series of experiments. The experimental results prove that our model is not only effective but also can provide stable and reliable solutions under different loading conditions and circumstances. These outcomes make us believe that the deep neural network model based on the Self-Attention mechanism has broad application prospects in dealing with the air cargo loading optimization problem. | en_US |